Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments Article Swipe
Janice Yang
,
Neil Daily
,
Taylor K. Pullinger
,
Tetsuro Wakatsuki
,
Eric A. Sobie
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1011806
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1011806
Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.
Related Topics
Concepts
Induced pluripotent stem cell
In silico
Cardiac electrophysiology
Electrophysiology
Biological system
Population
Computational biology
Computer science
Biology
Computational model
Genetic model
Bioinformatics
Neuroscience
Artificial intelligence
Embryonic stem cell
Genetics
Medicine
Environmental health
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pcbi.1011806
- OA Status
- gold
- Cited By
- 5
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402437249
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402437249Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pcbi.1011806Digital Object Identifier
- Title
-
Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experimentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-11Full publication date if available
- Authors
-
Janice Yang, Neil Daily, Taylor K. Pullinger, Tetsuro Wakatsuki, Eric A. SobieList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pcbi.1011806Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pcbi.1011806Direct OA link when available
- Concepts
-
Induced pluripotent stem cell, In silico, Cardiac electrophysiology, Electrophysiology, Biological system, Population, Computational biology, Computer science, Biology, Computational model, Genetic model, Bioinformatics, Neuroscience, Artificial intelligence, Embryonic stem cell, Genetics, Medicine, Environmental health, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.donor | 66 |
| abstract_inverted_index.found | 182 |
| abstract_inverted_index.iPSCs | 21 |
| abstract_inverted_index.known | 171 |
| abstract_inverted_index.labs. | 62 |
| abstract_inverted_index.model | 13, 138, 210, 214 |
| abstract_inverted_index.patch | 244 |
| abstract_inverted_index.since | 20 |
| abstract_inverted_index.these | 90, 105 |
| abstract_inverted_index.those | 179 |
| abstract_inverted_index.under | 191 |
| abstract_inverted_index.which | 236 |
| abstract_inverted_index.behind | 274 |
| abstract_inverted_index.buffer | 206 |
| abstract_inverted_index.fitted | 176, 227 |
| abstract_inverted_index.gained | 8 |
| abstract_inverted_index.higher | 241 |
| abstract_inverted_index.inform | 249 |
| abstract_inverted_index.models | 76, 91, 169 |
| abstract_inverted_index.pacing | 198 |
| abstract_inverted_index.silico | 158 |
| abstract_inverted_index.tackle | 104 |
| abstract_inverted_index.unseen | 217 |
| abstract_inverted_index.varied | 193 |
| abstract_inverted_index.Several | 74 |
| abstract_inverted_index.applied | 164, 258 |
| abstract_inverted_index.between | 61 |
| abstract_inverted_index.calcium | 188 |
| abstract_inverted_index.capture | 95 |
| abstract_inverted_index.cardiac | 15 |
| abstract_inverted_index.channel | 133, 202, 218, 268 |
| abstract_inverted_index.created | 156 |
| abstract_inverted_index.current | 37 |
| abstract_inverted_index.derived | 27 |
| abstract_inverted_index.disease | 16 |
| abstract_inverted_index.genetic | 67, 122 |
| abstract_inverted_index.healthy | 29 |
| abstract_inverted_index.iPSC-CM | 38, 78, 116, 140, 261 |
| abstract_inverted_index.induced | 1 |
| abstract_inverted_index.method, | 129 |
| abstract_inverted_index.methods | 40 |
| abstract_inverted_index.predict | 86 |
| abstract_inverted_index.produce | 41 |
| abstract_inverted_index.results | 57 |
| abstract_inverted_index.variety | 50 |
| abstract_inverted_index.various | 162 |
| abstract_inverted_index.voltage | 186 |
| abstract_inverted_index.without | 33 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.blockade | 203 |
| abstract_inverted_index.changing | 205 |
| abstract_inverted_index.combined | 199 |
| abstract_inverted_index.datasets | 159 |
| abstract_inverted_index.diseased | 31 |
| abstract_inverted_index.improved | 209 |
| abstract_inverted_index.invasive | 34 |
| abstract_inverted_index.observed | 99 |
| abstract_inverted_index.optimize | 144 |
| abstract_inverted_index.patients | 32 |
| abstract_inverted_index.pipeline | 111, 255 |
| abstract_inverted_index.powerful | 12 |
| abstract_inverted_index.protocol | 147 |
| abstract_inverted_index.surgery. | 35 |
| abstract_inverted_index.traction | 9 |
| abstract_inverted_index.algorithm | 123 |
| abstract_inverted_index.calibrate | 113 |
| abstract_inverted_index.datasets. | 180 |
| abstract_inverted_index.determine | 264 |
| abstract_inverted_index.developed | 82 |
| abstract_inverted_index.different | 260 |
| abstract_inverted_index.estimates | 212 |
| abstract_inverted_index.generates | 149 |
| abstract_inverted_index.heuristic | 126 |
| abstract_inverted_index.iPSC-CMs. | 101 |
| abstract_inverted_index.immature, | 44 |
| abstract_inverted_index.including | 196 |
| abstract_inverted_index.parameter | 127, 153, 211 |
| abstract_inverted_index.protocols | 53, 163 |
| abstract_inverted_index.research, | 19 |
| abstract_inverted_index.transient | 189 |
| abstract_inverted_index.(iPSC-CMs) | 6 |
| abstract_inverted_index.Therefore, | 252 |
| abstract_inverted_index.accessible | 239 |
| abstract_inverted_index.additional | 71 |
| abstract_inverted_index.developing | 108 |
| abstract_inverted_index.electrical | 197 |
| abstract_inverted_index.fetal-like | 45 |
| abstract_inverted_index.literature | 56 |
| abstract_inverted_index.maturation | 52 |
| abstract_inverted_index.mechanisms | 273 |
| abstract_inverted_index.normalized | 233 |
| abstract_inverted_index.parameters | 134, 177 |
| abstract_inverted_index.phenotypic | 59, 72, 97 |
| abstract_inverted_index.population | 167 |
| abstract_inverted_index.properties | 269 |
| abstract_inverted_index.responses, | 88 |
| abstract_inverted_index.responses. | 220, 278 |
| abstract_inverted_index.simulating | 161 |
| abstract_inverted_index.sufficient | 150 |
| abstract_inverted_index.suggesting | 231 |
| abstract_inverted_index.throughput | 242 |
| abstract_inverted_index.understand | 271 |
| abstract_inverted_index.backgrounds | 68 |
| abstract_inverted_index.calibrating | 184 |
| abstract_inverted_index.calibration | 128 |
| abstract_inverted_index.conditions, | 195 |
| abstract_inverted_index.conductance | 172, 250 |
| abstract_inverted_index.contributes | 69 |
| abstract_inverted_index.differences | 60 |
| abstract_inverted_index.limitations | 106 |
| abstract_inverted_index.normalized, | 230 |
| abstract_inverted_index.observation | 222 |
| abstract_inverted_index.parameters. | 118, 251 |
| abstract_inverted_index.phenotypes, | 47 |
| abstract_inverted_index.physiology. | 141 |
| abstract_inverted_index.pluripotent | 2 |
| abstract_inverted_index.predictions | 215 |
| abstract_inverted_index.recordings, | 235, 246 |
| abstract_inverted_index.variability | 98, 275 |
| abstract_inverted_index.variations, | 173 |
| abstract_inverted_index.calibration, | 154 |
| abstract_inverted_index.cell-derived | 4 |
| abstract_inverted_index.experimental | 146, 194 |
| abstract_inverted_index.fluorescence | 234 |
| abstract_inverted_index.individually | 92 |
| abstract_inverted_index.mathematical | 75, 137 |
| abstract_inverted_index.perturbation | 277 |
| abstract_inverted_index.preparations | 262 |
| abstract_inverted_index.sufficiently | 248 |
| abstract_inverted_index.therapeutics | 18 |
| abstract_inverted_index.variability. | 73 |
| abstract_inverted_index.Heterogeneity | 63 |
| abstract_inverted_index.computational | 110, 254 |
| abstract_inverted_index.line-specific | 266 |
| abstract_inverted_index.self-renewing | 23 |
| abstract_inverted_index.cardiomyocytes | 5, 42 |
| abstract_inverted_index.systematically | 143 |
| abstract_inverted_index.concentrations, | 208 |
| abstract_inverted_index.differentiation | 39 |
| abstract_inverted_index.electrophysiology | 79 |
| abstract_inverted_index.electrophysiological | 46, 117 |
| abstract_inverted_index.preparation-specific | 115 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.85122074 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |