Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals

Abstract

In recent years, optical imaging and efficient computational approaches have improved the ability to analyse and understand biological phenomena. Immunofluorescence (IF) is a widely used immunochemical technique that provides information about protein localisation and expression levels. However, the manual analysis of IF images can present important limitations, such as operator workload and interpretative bias. Thus, the development of automated tools for IF signal computation is crucial. Several software programs have been proposed to address this challenge, but there is still a need for more accurate and reliable systems. In this work, we present Q-IF, a software for automatically measuring cellular IF signals with an intuitive and easy-to-use interface. We describe the software and validate its results in different biological scenarios using SH-SY5Y neuroblastoma cells, human fibroblasts, and rat brain sections. The Q-IF system automatically carries out the entire process, from IF signal quantification to statistical analysis, thus evading operator biases and speeding up the analysis workflow. Our results demonstrate the accuracy and reliability of the Q-IF system, highlighting its potential as a valuable tool for IF analysis in biological research.

Keywords

• image processing
• immunofluorescence quantification
• artificial intelligence
• automatic quantification
• fluorescence intensity
• Q-IF