Automated C-FREE™ Workflow for Reproducible Memory T-cell and NK-cell Immunophenotyping 2306832

Abstract Introduction Manual staining and wash steps introduce variability in memory T-cell and NK-cell immunophenotyping, particularly when different operators prepare cocktails or perform sample preparation. Automated workflows can minimize this variability by standardizing staining, mixing, and washing conditions. This work evaluates whether automated antibody cocktailing and automated sample preparation improve workflow consistency for a multiparameter immunophenotyping panel. The current dataset uses a ∼9-marker T-cell panel, with NK-cell markers to be incorporated in the expanded evaluation. Methods PBMCs were stained under four preparation conditions: (1) automated cocktail + automated sample prep, (2) automated cocktail + manual sample prep, (3) manual cocktail + automated sample prep, (4) manual cocktail + manual sample prep. Cocktails were prepared manually or via the Pluto workstation; samples were processed using the C-FREE automated workflow or centrifuge-based washing. Quantitative endpoints included stain index (SI), retention, population frequencies, and scatter stability to illustrate performance across preparation conditions. Results Using the T-cell panel, stain index (SI), retention, and major population frequencies from automated cocktailing and automated sample preparation were all within ±20% of values obtained using manual cocktailing and manual sample preparation. Automated steps produced consistent replicate performance with stable scatter profiles. These results establish baseline workflow consistency for extending the evaluation to memory T-cell and NK-cell panels. Conclusion The automated C-FREE workflow, combined with automated antibody cocktailing, offers a reproducible approach for multiparameter immunophenotyping. By reducing manual variability and standardizing preparation steps, the workflow establishes a foundation for robust memory T-cell and NK-cell profiling in research and translational applications. Funding Source n/a Topic Categories Technological Innovations in Immunology (TECH)