CardioShift: Resolving cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes from high-throughput single-cell imaging
- 1University Medical Center Utrecht
Abstract
High-content screening of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is widely used for disease modeling, yet standard pipelines rely on whole-well averages that obscure cell-to-cell heterogeneity. Moving from well-averages to single-cell analysis reveals hidden variations, but may introduce a pseudoreplication bias if cells are analysed as independent data points. We present CardioShift, an open-source bioimage informatics framework designed to map single-cell distributions by properly accounting for this nested variance structure. Applying this framework to 32,818 Ca2+ transients and 10,719 contractility-lipid profiles, we show that cell-to-cell variability represents the largest source of variance for most functional parameters, demonstrating how traditional well averages mask condition-dependent phenotypes. Furthermore, unsupervised clustering identifies condition-dependent shifts in functional subpopulations, including a distinct cellular state that accumulates lipids but maintains baseline contraction kinetics that was not detected using well- and single-cell averaging approaches. CardioShift provides a robust analytical approach to resolve hidden single-cell distributions.
Related
- High-throughput, single-cell-encoded colony imaging reveals infection-linked heterogeneity
- Validation of a high throughput fluorescent Capillary Electrophoresis Sodium Dodecyl Sulfate method for monoclonal antibody size heterogeneity assessment
- Stimulatrix: An open-source automated platform for high-throughput functional characterization of engineered contractile tissues
- FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers
- High-throughput functional genomics and transcriptomics reveal molecular mechanisms regulating the synergistic fungicidal activity of copper and zinc
BibTeX
@misc{spanjersberg2026cardioshift,
title = {{CardioShift: Resolving cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes from high-throughput single-cell imaging}},
author = {T. C. F. Spanjersberg and C. N. van der Wilt and J.-C. Chang and J. B. Helms and R. G. C. Maas and J. P. G. Sluijter and V. Fontaine and E. Villard and T. P. de Boer and R. J. A. Veltrop and F. G. van Steenbeek and M. Harakalova},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.31.763029},
url = {https://www.biorxiv.org/content/10.64898/2026.10.31.763029v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - CardioShift: Resolving cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes from high-throughput single-cell imaging
AU - T. C. F. Spanjersberg
AU - C. N. van der Wilt
AU - J.-C. Chang
AU - J. B. Helms
AU - R. G. C. Maas
AU - J. P. G. Sluijter
AU - V. Fontaine
AU - E. Villard
AU - T. P. de Boer
AU - R. J. A. Veltrop
AU - F. G. van Steenbeek
AU - M. Harakalova
PY - 2026
DA - 2026/10/31
PB - bioRxiv
DO - 10.64898/2026.10.31.763029
UR - https://www.biorxiv.org/content/10.64898/2026.10.31.763029v1
AB - High-content screening of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is widely used for disease modeling, yet standard pipelines rely on whole-well averages that obscure cell-to-cell heterogeneity. Moving from well-averages to single-cell analysis reveals hidden variations, but may introduce a pseudoreplication bias if cells are analysed as independent data points. We present CardioShift, an open-source bioimage informatics framework designed to map single-cell distributions by properly accounting for this nested variance structure. Applying this framework to 32,818 Ca2+ transients and 10,719 contractility-lipid profiles, we show that cell-to-cell variability represents the largest source of variance for most functional parameters, demonstrating how traditional well averages mask condition-dependent phenotypes. Furthermore, unsupervised clustering identifies condition-dependent shifts in functional subpopulations, including a distinct cellular state that accumulates lipids but maintains baseline contraction kinetics that was not detected using well- and single-cell averaging approaches. CardioShift provides a robust analytical approach to resolve hidden single-cell distributions.
ER -