Introduction:
The limited success of pharmacological approaches to atrial fibrillation (
AF
) is due to limitations of
in vitro
and
in vivo
models and inaccessibility of human atrial tissue. Patient-specific induced pluripotent stem cell-derived atrial cardiomyocytes (iPSC-aCMs) are a robust platform to model the heterogeneous myocardial substrate of AF, but their immaturity limits their fidelity.
Objective:
We hypothesized that a combinatorial approach of biochemical (triiodothyronine [
T3
], insulin-like growth factor-1 [
IGF-1
], and dexamethasone; collectively
TID
), bioenergetic (fatty acids [
FA
]), and electrical stimulation (
ES
) will enhance electrophysiological (
EP
), structural, and metabolic maturity of iPSC-
a
CMs.
Methods:
We assessed maturation with whole cell patch clamping, calcium transients, immunofluorescence (IF), Seahorse Analyzer, contractility assay, RT-PCR, Western Blotting, and RNA sequencing (RNAseq). Using a time series with RNAseq we identified signaling pathways and transcriptional regulation that drive EP, structural, and metabolic atrial development and compared iPSC-aCM maturity with human aCMs (haCMs) obtained from the same patient.
Results:
TID+FA+ES significantly improved structural organization and cell morphology (
Fig. 1a
), enhanced membrane potential stability and improved depolarization (
Fig. 1b
), improved Ca
2+
kinetics with faster and increased Ca
2+
release from sarcoplasmic reticulum (
Fig. 1c
), and increased expression of Na
+
, Ca
2+
, and K
+
channels, markers of structural maturity, FA metabolism, and oxidative phosphorylation (
Fig. 1d
). There was no difference in each parameter between TID+FA+ES iPSC-aCMs and haCMs from the same patient.
Conclusion:
Our optimized, combinatorial TID+FA+ES approach markedly enhanced EP, structural, and metabolic maturity of human iPSC-aCMs, which will be useful for elucidating the genetic basis of AF developing precision drug therapies.
Human pluripotent stem cell-derived kidney organoids for personalized congenital and idiopathic nephrotic syndrome modeling