A Modular High‐Parameter Flow Cytometry Framework: Pre‐Analytical Optimization and Validation for Clinical Research

High-parameter flow cytometry (hpFCM) enables powerful immune profiling and biomarker discovery in clinical trials. However, its clinical adoption is hindered by assay variability, logistical challenges, and technical complexity. Ensuring data reproducibility and robustness requires stringent quality controls and standardized validation. We introduce a new concept for implementing hpFCM in drug development, a modular hpFCM approach. It features a 12-marker core panel with 21 additional markers distributed in five interchangeable modules, enabling flexible assay customization throughout clinical studies. This innovative design allows for dynamic modifications during clinical development, facilitating evolving immunophenotyping of T and NK cells and comprehensive biomarker analysis. Validation in accordance with CLSI H62 guidelines confirmed the assay's reliability across exploratory and secondary endpoints, demonstrating high accuracy, selectivity, repeatability, and reproducibility independent of the applied assay configuration. Key secondary endpoints, including CD8+ cytotoxic and regulatory T cells, achieved precision, with repeatability and inter-operator variability under 20% coefficient of variation (CV), while specimen stability was maintained over 4 days of ambient storage. For exploratory endpoints, 90% of analytes met repeatability criteria, with 55% remaining stable over 4 days. The modular approach enhances end-to-end solutions and assay adaptability, supporting both targeted and exploratory strategies for clinical biomarker analysis. Importantly, robust performance and data comparability among different assay configurations were confirmed in both whole blood and frozen PBMC samples. By addressing key limitations in current hpFCM workflows, this modular approach provides a scalable, agile, and cost-effective framework designed to support biomarker strategies during drug development. This framework was conceptualized to increase operational efficiency and enable reverse translational approaches by allowing the rapid integration of emerging markers. Consequently, this design facilitates dynamic immunophenotyping and biomarker discovery, streamlining the path from exploratory research to clinical implementation. Importantly, while the assay is designed for biomarker discovery and exploratory immune monitoring in clinical trials, its clinical utility, therapeutic-response performance, and feasibility in multi-site real-world trial logistics remain to be established in ongoing studies involving specific disease cohorts.

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Publication Details

Journal
Cytometry Part A
Published
2026-06-26
DOI
https://doi.org/10.1002/cyto.a.70049
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
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A Modular High‐Parameter Flow Cytometry Framework: Pre‐Analytical Optimization and Validation for Clinical Research

Enrique Gómez Alcaide, Tobias Rutishauser, Iva Lelios, Megan McCausland et al.
Cytometry Part A
Single-cell and spatial transcriptomics
article

A Modular High‐Parameter Flow Cytometry Framework: Pre‐Analytical Optimization and Validation for Clinical Research

Enrique Gómez Alcaide, Tobias Rutishauser, Iva Lelios, Megan McCausland, Stefan Ganter, Karen Smith, Jennifer Scott, Alistair Watt
article en

Abstract

High-parameter flow cytometry (hpFCM) enables powerful immune profiling and biomarker discovery in clinical trials. However, its clinical adoption is hindered by assay variability, logistical challenges, and technical complexity. Ensuring data reproducibility and robustness requires stringent quality controls and standardized validation. We introduce a new concept for implementing hpFCM in drug development, a modular hpFCM approach. It features a 12-marker core panel with 21 additional markers distributed in five interchangeable modules, enabling flexible assay customization throughout clinical studies. This innovative design allows for dynamic modifications during clinical development, facilitating evolving immunophenotyping of T and NK cells and comprehensive biomarker analysis. Validation in accordance with CLSI H62 guidelines confirmed the assay's reliability across exploratory and secondary endpoints, demonstrating high accuracy, selectivity, repeatability, and reproducibility independent of the applied assay configuration. Key secondary endpoints, including CD8+ cytotoxic and regulatory T cells, achieved precision, with repeatability and inter-operator variability under 20% coefficient of variation (CV), while specimen stability was maintained over 4 days of ambient storage. For exploratory endpoints, 90% of analytes met repeatability criteria, with 55% remaining stable over 4 days. The modular approach enhances end-to-end solutions and assay adaptability, supporting both targeted and exploratory strategies for clinical biomarker analysis. Importantly, robust performance and data comparability among different assay configurations were confirmed in both whole blood and frozen PBMC samples. By addressing key limitations in current hpFCM workflows, this modular approach provides a scalable, agile, and cost-effective framework designed to support biomarker strategies during drug development. This framework was conceptualized to increase operational efficiency and enable reverse translational approaches by allowing the rapid integration of emerging markers. Consequently, this design facilitates dynamic immunophenotyping and biomarker discovery, streamlining the path from exploratory research to clinical implementation. Importantly, while the assay is designed for biomarker discovery and exploratory immune monitoring in clinical trials, its clinical utility, therapeutic-response performance, and feasibility in multi-site real-world trial logistics remain to be established in ongoing studies involving specific disease cohorts.

Cytometry Part A
Roche (Switzerland) (CH), IQVIA (United Kingdom) (GB), IQVIA (United States) (US)
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
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