BeatRatePy - Automated Hight-Throughput Python-Based Beat Rate Quantification and Generation of Speed and Contraction Traces
- 1University of Liverpool
Abstract
Analysing cardiac motion from label free imaging can be difficult, especially when recordings vary in quality or show complex movement. We developed a Python-based pipeline that automatically measures beat rate, contraction, speed from transmitted light timelapse data. The workflow runs in a Jupyter notebook and uses straightforward steps that can be followed easily, making the analysis accessible including to users with limited coding experience. Using human iPSC-derived cardiomyocytes imaged on a Zeiss Cell Discoverer 7, the pipeline produced beat rates, contraction and contraction speed measurements that matched expected behaviour. To test wider use, we applied the same workflow to killifish larvae heart recordings and public datasets, changing only basic input parameters. The pipeline performed consistently across these different systems and did not require changes to the underlying code. By supporting clear parameter control and use across laboratories, microscopy platforms and repositories, this approach strengthens reproducible analysis of cardiac motion from label free imaging.
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BibTeX
@misc{held2026beatratepy,
title = {{BeatRatePy - Automated Hight-Throughput Python-Based Beat Rate Quantification and Generation of Speed and Contraction Traces}},
author = {M. Held and W. Partington and T. Waring and A. Dev and L. Isherwood and B. Boulton and P. Sharma and R. Herbert and T. Kudoh and A. D. Corbett and R. A. B. Burton},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.21.760713},
url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760713v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - BeatRatePy - Automated Hight-Throughput Python-Based Beat Rate Quantification and Generation of Speed and Contraction Traces
AU - M. Held
AU - W. Partington
AU - T. Waring
AU - A. Dev
AU - L. Isherwood
AU - B. Boulton
AU - P. Sharma
AU - R. Herbert
AU - T. Kudoh
AU - A. D. Corbett
AU - R. A. B. Burton
PY - 2026
DA - 2026/10/21
PB - bioRxiv
DO - 10.64898/2026.10.21.760713
UR - https://www.biorxiv.org/content/10.64898/2026.10.21.760713v1
AB - Analysing cardiac motion from label free imaging can be difficult, especially when recordings vary in quality or show complex movement. We developed a Python-based pipeline that automatically measures beat rate, contraction, speed from transmitted light timelapse data. The workflow runs in a Jupyter notebook and uses straightforward steps that can be followed easily, making the analysis accessible including to users with limited coding experience. Using human iPSC-derived cardiomyocytes imaged on a Zeiss Cell Discoverer 7, the pipeline produced beat rates, contraction and contraction speed measurements that matched expected behaviour. To test wider use, we applied the same workflow to killifish larvae heart recordings and public datasets, changing only basic input parameters. The pipeline performed consistently across these different systems and did not require changes to the underlying code. By supporting clear parameter control and use across laboratories, microscopy platforms and repositories, this approach strengthens reproducible analysis of cardiac motion from label free imaging.
ER -