Standardizing a human TBMNK cell assay across instrument platforms: a new framework for producing high-quality, AI-ready reference dataset

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Abstract

Introduction: Recent advances in regenerative medicine advanced therapies (RMAT) are revolutionizing health care by providing curative treatments for previously untreatable diseases. Flow cytometry assays have been used to measure critical quality attributes, including viability, identity, purity, strength, and potency of the RMAT. However, the lack of reproducibility, comparability, and measurement confidence of results across various flow cytometry platforms, sites, and over time remain a major challenge. Method: Through a large-scale interlaboratory study (ILS), coordinated by the NIST Flow Cytometry Standards Consortium, a public-private partnership, we carried out study to identify T/B/Monocyte/Natural Killer (TBMNK) cells and to measure cell viability and health status. This ILS used common study samples, associated instrument control- and assay-reagents, and four standard operating procedures (SOPs) to enable end-to-end assay standardization across 42 cytometers from 7 manufacturers across 21 sites. Results: We present the first demonstration for achieving standardized cytometric assays across a wide range of instrument platforms. The results of the ILS were reported in quantitative units with metrological traceability to the SI (International System of Units): number of cells per µL for cell count and Equivalent Reference Fluorophore (ERF) units for expression levels of CD markers. The standardization approach puts flow cytometric data from different instrument platforms on the standardized ERF scale. Discussion: To overcome the challenge of instrument-driven variability, this ILS utilizes a human TBMNK cell assay to establish and validate a new standardization framework. By reporting results in SI-traceable units, this work demonstrates that comparable measurements of viable, non-apoptotic TBMNK subsets are achievable across different cytometers. The resulting high-quality, artificial intelligence (AI)-ready reference dataset provides a critical foundation for the validation of AI/Machine Learning (ML) technologies in translational immunology. Additionally, this study underscores that the integration of automated sample preparation is a necessary next step to further minimize assay uncertainty and enhance cross-platform reproducibility.

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Regenerative medicine and advanced therapy, Reference data, Health, Flow cytometry, Clinical diagnostics, Cell measurements, Cell and gene therapy, Biometrology, Biomaterials and Biomanufacturing

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