Deciphering the internal complexity of living cells with quantitative phase microscopy: a multiscale approach

Area of Science:

• Biophysics
• Cell Biology
• Optical Imaging

Background:

• Interpreting living cell optical phase images is challenging due to limited knowledge of refractive index (RI) contributions and mixed phase/diffraction effects.
• Internal macromolecular complexes significantly impact local RI, complicating image analysis.
• Existing methods struggle to disentangle RI variations from diffraction, hindering accurate structural recovery.

Purpose of the Study:

• To develop and implement a novel 2D wavelet-based contour chain detection method for analyzing living cell optical phase images.
• To identify internal cell boundaries based on optical path difference gradients, correlating them with RI inhomogeneities.
• To establish morphological indicators for comparing cell origins and tracking pathological transformations.

Main Methods:

• A two-dimensional wavelet-based contour chain detection algorithm was employed.
• The method identifies internal cell boundaries by detecting greatest optical path difference gradients.
• Analysis focused on correlating detected contour chains with local RI variations and intracellular structural complexity.

Main Results:

• The wavelet-based method successfully distinguished internal cell boundaries based on phase contrast.
• Detected contour chains mapped to RI inhomogeneities, reflecting intracellular structural intricacy.
• The method's statistics and spatial distribution provided morphological indicators for cell comparison and transformation assessment.

Conclusions:

• The proposed wavelet-based contour chain detection is effective for analyzing optical phase images of living cells.
• This method offers a way to quantify intracellular structural complexity and RI distribution.
• The approach shows promise for comparing cell origins and monitoring cellular changes in pathological conditions, such as leukemia development.