# 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-SYSY 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. image processing; immunofluorescence quantification; artificial intelligence; automatic quantification; fluorescence intensity; Q-IF; cell image analysis +
Footnote †: journal: Medical  
levels. Presently, the prevailing approach involves manual pixel analysis using software like ImageJ [4]. However, this method has significant limitations, as it frequently requires repetitive and tedious work from the operator, and it can be prone to interpretation bias. Therefore, a key challenge lies in developing automated tools that can accurately identify and quantify signals while minimising the need for user intervention [5]. By implementing automation, both productivity and statistical rigour can be ensured, as it allows for objective analysis, even with large datasets. Moreover, automated digital image analysis guarantees the accuracy, objectivity, and reproducibility of the obtained data. Many of the different methodologies capable of automatically or semi-automatically analysing images of biological samples are based on segmentation methods. Automatic cell segmentation involves the extrapolation of single cells (regions of interest, ROIs) from the background and the discrimination of multiple cells if they are closely attached [6]. Among the various tools available, a significant contribution to advancing the field of image segmentation is provided by [PERSON] and collaborators' software. Seg2 Link relies on deep learning to make cell/non-cell predictions and then performs automatic instance segmentation using watershed 2D to divide the cell regions into separate cells. Furthermore, the user can manually correct the automatic segmentation results of each section, thus helping the software to improve the quality of subsequent operations and reducing the corrections required [7]. An important contribution to the progress of automated IF signal analysis is the work conducted by [PERSON] et al., resulting in software designed to automatically identify and quantify signals in multichannel IF images. This software allows for the analysis of the percentage of high-intensity signals and low-intensity signals by evaluating stained pixels associated with marker outcomes [8]. In the same domain, [PERSON] et al. released an open-source software called AQUISTO, which offers an automated approach for quantifying IF in different tissue sections [9]. Additionally, [PERSON] et al. developed software named IFDOTMETER for quantifying various biological markers. This software enables measurements of cell area, autophagy markers like the number of LC3-puncta, nuclear condensation, and the assessment of mitochondrial morphology. Nonetheless, as the authors report, there are some cautions to consider before performing the analysis.