High-content imaging can capture detailed morphological and functional features on a single-cell level. It has been widely used in drug screen assays and for generating large-scale image datasets for machine learning models. Software such as CellProfiler extract features from high-content images for downstream analysis. CellProfiler implements a suite of traditional image processing algorithms to segment cells and extract about 1,000 morphological and texture features. To simplify data analysis of CellProfiler features in R—many options exist in Python, such as PyCytominer—we developed an R/Bioconductor package. Our package leverages Bioconductor objects for batch correction and dimension reduction, lowering the learning curve for users familiar with single-cell RNA sequencing workflows. Our package is available at https://github.com/christofseiler/cellpaintr. Presented at the European Bioconductor Conference (EuroBioC2025), Barcelona, September 2025.