Optimizing Fiji/ImageJ for Accurate Cell Counting and Differentiation Analysis of Induced Pluripotent Stem Cells (iPSCs)

This study aims to develop a reliable and efficient methodology for accurately counting induced pluripotent stem cells (iPSCs) and their differentiated counterparts using Fiji (ImageJ), a widely used image-processing tool in cell and molecular biology. The primary objective is to optimize parameters such as thresholding, contrasts, and region-of-interest (ROI) identification within Fiji to distinguish between iPSCs and differentiated iPSCs. The methodology will address challenges on why current automated programs are unable to accurately count cells. Current limitations for counting include cell density, overlapping cells, and differences in cell morphology between iPSCs. Training machine learning using diverse images to accurately count cells can ensure accurate cell counts and quality control. The goal is to establish a consistent procedure that ensures precise and reproducible cell counts, contributing to more accurate analyses of iPSC differentiation and its potential applications in regenerative medicine. This experiment is crucial because accurate cell counting and differentiation analysis are essential for studying iPSCs and their applications in regenerative medicine. iPSCs have the potential to develop into various cell types, making them valuable for disease modeling, drug testing, and potential therapeutic applications.