Abstract
High-content screening (HCS) enables the systematic quantification of single-cell morphology features across thousands of perturbations, capturing rich phenotypic heterogeneity. Image-based profiling is a critical bioinformatics processing step in this pipeline, as researchers use it to predict mechanisms of action, assess toxicity, perform hit calling, and more. However, current image-based profiling workflows rely on aggregate statistics, such as calculating mean or median feature values per well, implicitly assuming cell homogeneity. This limitation obscures subpopulation effects, reducing sensitivity to subtle or heterogeneous effects of perturbations. Here we present Buscar, a method that leverages the full heterogeneity of single-cell image-based profiles to call hits. Buscar requires two reference, single-cell populations that define distinct morphology states: a reference state (e.g., disease cells) and a target state (e.g., healthy cells). Buscar then compares these two groups to define on- and off-morphology signatures, which it then uses to score every perturbation in a given screen. The scores quantify perturbation efficacy and off-target effects, or specificity, in an interpretable manner, clarifying which morphologies are appropriately altered and which may arise from off-target activity. We apply Buscar to three datasets. First, as a proof of concept, we applied Buscar to a Cell Painting dataset of cardiac fibroblasts from patients with heart failure. Buscar quantifies both morphology rescue and off-target morphology activity in these cells treated with a TGFβ receptor inhibitor. Second, we show that Buscar recovers biologically coherent gene-phenotype associations across 16 manually-labeled phenotypes in the MitoCheck dataset. Lastly, applied to CPJUMP1, we show that Buscar is robust to technical replicates collected across plates in both small-molecule and CRISPR-Cas9 perturbations. Together, these results establish Buscar as a reproducible and interpretable hit calling method that overcomes aggregation bias, enabling the simultaneous quantification of compound efficacy and specificity to enhance hit calling in HCS. We release Buscar as an open-source python package.