Statistical lower bounds on protein copy number from fluorescence expression images

Area of Science:

• Molecular and Systems Biology
• Developmental Biology
• Biophysics

Background:

• Fluorescence imaging is widely used for quantifying mRNA or protein expression.
• Current methods often provide relative, not absolute, expression values.
• Absolute expression measures are essential for quantitative network modeling and noise analysis.

Purpose of the Study:

• To propose and validate methods for estimating absolute molecular copy numbers from uncalibrated fluorescence images.
• To enable more precise quantitative analysis in systems biology and developmental studies.

Main Methods:

• Developed two novel methods leveraging cell-to-cell expression variability (steady-state fluctuations or cell division distribution).
• Applied methods to 152 protein fluorescence expression images of Drosophila melanogaster embryos.
• Generated molecular copy number estimates for 14 genes in the segmentation network.

Main Results:

• Successfully estimated molecular copy numbers for 14 genes in Drosophila melanogaster embryos.
• Analyzed the impact of noise on the developed estimators.
• Confirmed that steady-state expression variance scales with mean expression, consistent with previous findings.

Conclusions:

• The proposed methods provide a robust way to determine absolute molecular copy numbers from fluorescence imaging data.
• These estimates are valuable for quantitative network modeling and understanding gene expression noise.
• The findings contribute to a deeper understanding of gene regulatory networks in early development.