BeatRatePy - Automated Hight-Throughput Python-Based Beat Rate Quantification and Generation of Speed and Contraction Traces

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. NEW & NOTEWORTHY We have established a protocol for label-free transmitted light imaging on a Zeiss Cell Discoverer 7 platform which enables contraction visualisation and quantification of human iPSC-derived cardiomyocytes (hiPSC-CMs). This provides the input for a new, user-friendly Python script based on the Pixel Variance algorithm, that has been developed to enable automatic batch processing of recordings to analyse beat rate and contraction.