Developing a Label-Free High-Throughput Flow Assay by Imaging Cytometry on the Attune CytPix 2300698

Abstract

Flow cytometry is a primary tool for characterizing immune cell dynamics in high-throughput screening (HTS) assays. However, traditional staining and acquisition procedures demand substantial antibodies, resources, and time. Moreover, HTS assays are often constrained to a minimal set of markers, limiting biological readouts to largely binary outputs (e.g., activated vs. resting) and oversimplifying the complexity of drug responses. To simplify assays while improving biological insights, we leveraged the imaging capabilities of the Attune CytPix to investigate whether imaging flow cytometry can replace activation and viability markers and detect complex phenotypes based on cellular morphology. Using brightfield imaging-derived parameters and UMAP dimensionality reduction analysis, we successfully resolved distinct clusters separating human resting Peripheral Blood Mononuclear Cells (PBMC) from activated CD25+CD69+ PBMCs. Additionally, cell debris and morphologically distinct dead cells formed separate populations, suggesting the ability to differentiate modalities of cell death through imaging alone. We then applied an AI-driven Python-based analysis to the CytPix derived images, and successfully reconstructed label-free drug-response curves comparable to those derived via conventional manual gating in standard flow cytometry. Building on these findings, we are now integrating additional imaging-derived parameters to uncover phenotypes that may not be captured by conventional marker-based approaches, with the goal of enabling richer, label-free HTS readouts at scale. n/a Technological Innovations in Immunology (TECH)