Geometric compensation applied to image analysis of cell populations with morphological variability: a new role for a classical concept

Joana Figueiredo,João M Sanches,Soraia Melo,Maria Sofia Fernandes,Isabel Rodrigues,Bárbara Sousa,João Ribeiro,Raquel Seruca,Joana Paredes

doi:10.1038/s41598-018-28570-z

Abstract

Immunofluorescence is the gold standard technique to determine the level and spatial distribution of fluorescent-tagged molecules. However, quantitative analysis of fluorescence microscopy images faces crucial challenges such as morphologic variability within cells. In this work, we developed an analytical strategy to deal with cell shape and size variability that is based on an elastic geometric alignment algorithm. Firstly, synthetic images mimicking cell populations with morphological variability were used to test and optimize the algorithm, under controlled conditions. We have computed expression profiles specifically assessing cell-cell interactions (IN profiles) and profiles focusing on the distribution of a marker throughout the intracellular space of single cells (RD profiles). To experimentally validate our analytical pipeline, we have used real images of cell cultures stained for E-cadherin, tubulin and a mitochondria dye, selected as prototypes of membrane, cytoplasmic and organelle-specific markers. The results demonstrated that our algorithm is able to generate a detailed quantitative report and a faithful representation of a large panel of molecules, distributed in distinct cellular compartments, independently of cell’s morphological features. This is a simple end-user method that can be widely explored in research and diagnostic labs to unravel protein regulation mechanisms or identify protein expression patterns associated with disease.

Highlights

Immunofluorescence (IF) microscopy is a widely used technique that uses fluorescent-labelled markers to visualize the distribution of proteins, glycoproteins and other molecular targets in intracellular structures, at the cellular level or at the tissue level[1,2]
We have developed a bioimaging tool to assess the patterns of expression of CDH1 germline missense variants associated to a cancer syndrome[8]
The networks are independent of the non-regular distribution of cells and avoid the need of repetitive and predictive patterns to define a neighbouring system

Summary

Introduction

Immunofluorescence (IF) microscopy is a widely used technique that uses fluorescent-labelled markers to visualize the distribution of proteins, glycoproteins and other molecular targets in intracellular structures, at the cellular level or at the tissue level[1,2]. Different morphological features will give rise to a high variability in the expression profiles, impairing the extraction of a representative overview/map of a particular target within a heterogeneous cell population To overcome this constraint on endogenous cell-to-cell differences, we developed a geometric compensation model specific for in situ IF applications. The method applies a Bayesian non-rigid alignment algorithm and an automatic outlier rejection strategy to a large number of individual profiles, minimizing the undesired effect of size and shape variability within the cell population. In this way, the algorithm generates a final profile which is a distorted version of an ideal unknown profile, representative of all cells analysed within an IF image

Methods

Results

Conclusion