Biochemical Oscillations in HeLa Cells: Automated Time-Lapse Image Analysis and Two-Step Parameter Estimation

Area of Science:

• Biophysics
• Computational Biology
• Cancer Research

Background:

• Single-cell analysis from time-lapse imaging is crucial for understanding heterogeneous cell systems, particularly in cancer.
• Calcium oscillations encode cell states, but manual analysis of imaging data and parameter estimation for dynamic models are time-consuming and challenging.
• Existing methods struggle with manual segmentation and the complex parameter space of nonlinear differential equations (ODEs).

Purpose of the Study:

• To develop an automated pipeline for analyzing single-cell calcium dynamics from live cell imaging.
• To address the challenges of manual segmentation and parameter estimation in nonlinear dynamic models.
• To improve the characterization of cell states and advance cancer treatment strategies.

Main Methods:

• Utilized YOLOv9, a computer vision algorithm, for automated segmentation and calcium signal extraction from time-lapse microscopy videos.
• Implemented a two-step parameter estimation strategy combining Monte Carlo (MC) simulations and Genetic Algorithms (GA).
• MC simulations were used to define optimal search bounds for GA, which then estimated parameters for the nonlinear ODE model.

Main Results:

• YOLOv9 successfully automated calcium signal extraction, significantly reducing analysis time.
• The combined MC and GA approach demonstrated a significant reduction in Kullback-Leibler (KL) divergence compared to using GA alone.
• The estimated parameters were used to simulate the stochastic version of the model, validating the approach.

Conclusions:

• The proposed automated pipeline, integrating YOLOv9 and a hybrid MC-GA method, offers a robust and efficient approach for single-cell dynamic analysis.
• This methodology holds significant promise for deciphering complex cell states and informing the development of targeted cancer therapies.
• Automated analysis of calcium imaging data can accelerate biological discovery and improve the understanding of cellular dynamics.