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A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies
Sabine Ivison, … , Ryan R. Brinkman, Megan K. Levings
Sabine Ivison, … , Ryan R. Brinkman, Megan K. Levings
Published December 6, 2018
Citation Information: JCI Insight. 2018;3(23):e121867. https://doi.org/10.1172/jci.insight.121867.
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Technical Advance Immunology Transplantation

A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies

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Abstract

The analysis and validation of flow cytometry–based biomarkers in clinical studies are limited by the lack of standardized protocols that are reproducible across multiple centers and suitable for use with either unfractionated blood or cryopreserved PBMCs. Here we report the development of a platform that standardizes a set of flow cytometry panels across multiple centers, with high reproducibility in blood or PBMCs from either healthy subjects or patients 100 days after hematopoietic stem cell transplantation. Inter-center comparisons of replicate samples showed low variation, with interindividual variation exceeding inter-center variation for most populations (coefficients of variability <20% and interclass correlation coefficients >0.75). Exceptions included low-abundance populations defined by markers with indistinct expression boundaries (e.g., plasmablasts, monocyte subsets) or populations defined by markers sensitive to cryopreservation, such as CD62L and CD45RA. Automated gating pipelines were developed and validated on an independent data set, revealing high Spearman’s correlations (rs >0.9) with manual analyses. This workflow, which includes pre-formatted antibody cocktails, standardized protocols for acquisition, and validated automated analysis pipelines, can be readily implemented in multicenter clinical trials. This approach facilitates the collection of robust immune phenotyping data and comparison of data from independent studies.

Authors

Sabine Ivison, Mehrnoush Malek, Rosa V. Garcia, Raewyn Broady, Anne Halpin, Manon Richaud, Rollin F. Brant, Szu-I Wang, Mathieu Goupil, Qingdong Guan, Peter Ashton, Jason Warren, Amr Rajab, Simon Urschel, Deepali Kumar, Mathias Streitz, Birgit Sawitzki, Stephan Schlickeiser, Janetta J. Bijl, Donna A. Wall, Jean-Sebastien Delisle, Lori J. West, Ryan R. Brinkman, Megan K. Levings

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Figure 7

Generation of automated data analysis pipelines for the DuraClone IM Basic panel.

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Generation of automated data analysis pipelines for the DuraClone IM Bas...
The proportions of cell populations indicated in Supplemental Table 1 for the Basic panel were determined in unfractionated blood from healthy (n = 9) and HSCT (n = 11) subjects using automated gating, 2 different manual analyzers (Manual 1, Manual 2), or a reference manual analyzer. Data from Manual 1 or 2, or automated gating were compared with those from the reference manual to determine (A) Spearman’s correlation coefficients (rs) or (B) F1 scores (harmonization of precision and recall, maximum value 1.0) ordered by population size (highest on the left). Box-and-whisker plot: midline is the median, box is the IQR, and the whiskers extend to 1.5 times the IQR. (C) Representative automated or manually gated data from 1 healthy subject and correlation graphs for (C) CD14+ total monocytes, (D) CD14+CD16+ monocytes, and (E) CD56++ NK cells. White circles indicate Manual 1, and gray squares automated, both plotted against the reference manual data.
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