Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca²⁺ Imaging Datasets

Area of Science:

• Cellular biology
• Physiology
• Biophysics

Background:

• Intracellular calcium (Ca²⁺) signals regulate critical cellular functions, including contraction, secretion, and proliferation.
• Analyzing Ca²⁺ signals, especially stochastic or rapid subcellular events, using fluorescence microscopy often requires time-consuming manual curation by investigators.
• Existing methods struggle with the complexity and speed of Ca²⁺ signal analysis in vascular myocytes.

Purpose of the Study:

• To evaluate the accuracy and reliability of automated image analysis workflows for Ca²⁺ fluorescence data in vascular myocytes.
• To determine if automated analysis can replace manual curation for both full-frame time-series and line-scan Ca²⁺ imaging data.
• To assess the potential of automation to improve the efficiency of Ca²⁺ imaging data analysis.

Main Methods:

• Re-analysis of a published "gold standard" dataset of Ca²⁺ signals from pulmonary arterial myocytes using automated image analysis software (LCPro for ImageJ and SparkLab 5.8).
• Automated detection and filtering of oscillatory Ca²⁺ events (4–40 s duration) and rapid subcellular Ca²⁺ "spark" events.
• Comparison of automated results against manually curated data to calculate metrics like positive predictive value, sensitivity, and false discovery rates.

Main Results:

• Automated analysis showed very few significant differences compared to manual curation for both oscillatory and Ca²⁺ spark events.
• No systematic biases were observed in the data curation or filtering techniques used in the automated methods.
• The automated methods demonstrated high fidelity in capturing spatial and temporal aspects of Ca²⁺ imaging data.

Conclusions:

• Automated analysis techniques can reliably quantify Ca²⁺ imaging data, including oscillatory and spark events, in vascular myocytes.
• The accuracy of automated analysis is comparable to manual curation, validating its use in research.
• Implementing automated analysis workflows can significantly enhance experimental efficiency without compromising data quality.