Cellular and Nuclear Alignment Analysis for Determining Epithelial Cell Chirality.

Area of Science:

• Developmental Biology
• Biophysics
• Cell Biology

Background:

• Left-right (LR) asymmetry is fundamental in organisms, influencing organ arrangement and tissue morphogenesis.
• Cellular alignment and chirality are key indicators of LR asymmetry in embryonic tissues.
• Previous methods lacked detailed characterization of individual cell morphology and chirality on micropatterned surfaces.

Purpose of the Study:

• To develop and validate a Python-based algorithm for quantifying individual epithelial cell morphology and chirality.
• To analyze cell and nucleus alignment on micropatterned surfaces.
• To investigate the relationship between cell morphology and chiral bias.

Main Methods:

• Development of a Python algorithm for identifying and quantifying individual epithelial cells from immunofluorescence images.
• Application of the algorithm to analyze cell area, aspect ratio, and nucleus alignment on micropatterned substrates.
• Comparison of the algorithm's results with existing image intensity gradient-based methods.

Main Results:

• The algorithm accurately quantifies individual cell properties like area and aspect ratio.
• Biased alignment of cell nuclei was observed, with approximately 35% of cells showing misalignment.
• Misaligned cells were typically smaller and less elongated than aligned cells.
• The new approach yields results comparable to established methods while providing single-cell resolution.

Conclusions:

• The developed Python algorithm is an effective tool for measuring single-cell chirality within multicellular structures.
• This method can help elucidate the biophysical mechanisms driving cellular chiral bias.
• The findings contribute to a deeper understanding of LR asymmetry in both in vitro and in vivo systems.