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Related Experiment Video

Updated: Jun 10, 2026

Fluorescence-Based Calcium Imaging in Primary Human Airway Epithelial Cultures Using Automated Cell Segmentation
09:04

Fluorescence-Based Calcium Imaging in Primary Human Airway Epithelial Cultures Using Automated Cell Segmentation

Published on: May 22, 2026

Fluorescence-Based Calcium Imaging in Primary Human Airway Epithelial Cultures Using Automated Cell Segmentation.

Chiara D'Addario1, Abdelkader Daoud2, Christine E Bear3

  • 1Molecular Medicine, Hospital for Sick Children; Department of Biochemistry, University of Toronto.

Journal of Visualized Experiments : Jove
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed new methods to study calcium signaling in airway epithelial cells. This approach uses machine learning for precise, single-cell analysis in primary tissues, advancing biological process research.

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Last Updated: Jun 10, 2026

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Area of Science:

  • Cell Biology
  • Biophysics
  • Physiology

Background:

  • Calcium signaling is vital for numerous biological processes.
  • Studying calcium flux at single-cell resolution in primary epithelial tissues is technically difficult.
  • Existing methods are insufficient for detailed analysis of calcium dynamics in complex primary cultures.

Purpose of the Study:

  • To establish and validate methods for monitoring live calcium signaling in primary airway epithelial cultures.
  • To develop novel machine learning-based analytical approaches for calcium flux data.
  • To enable high-resolution, unbiased analysis of calcium dynamics in individual cells within primary epithelial models.

Main Methods:

  • Utilized patient-derived primary airway epithelial cultures differentiated at air-liquid interface (ALI).
  • Employed an extrinsic fluorescent indicator dye to visualize calcium mobilization.
  • Measured calcium flux at single-cell resolution using epifluorescent microscopy.
  • Developed novel software incorporating machine learning for cell segmentation and fluorescence intensity tracking over time.

Main Results:

  • Successfully adapted imaging setup for live calcium signaling monitoring in primary airway epithelial cultures.
  • Developed a machine learning-based software for accurate cell segmentation and fluorescence quantification.
  • Demonstrated a rapid and unbiased method for analyzing single-cell calcium dynamics.
  • Established a protocol applicable to diverse primary epithelial cell types.

Conclusions:

  • The presented methods provide a robust framework for studying calcium signaling in primary airway epithelial cells.
  • The novel software facilitates high-throughput, single-cell analysis of calcium dynamics.
  • This work overcomes previous limitations, paving the way for deeper investigation into cellular calcium roles.