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<p>Cell populations in flow cytometry are typically identified via visual manual gating, a time-consuming and error-prone approach to select subpopulations based on expression of cellular markers. Batch processing can be used to automate the analysis of bimodally distributed data but underperforms with highly-variable or continuously-expressed markers. We developed a visual pattern recognition automated gating tool, BD ElastiGate Software (hereafter ElastiGate), to recapitulate the visual process of manual gating by automatically adjusting gates to capture local variability. ElastiGate converts histograms and two-dimensional plots into images, then uses elastic B-spline image registration to transform pre-gated training plot images and their gates to corresponding ungated target plot images, thereby adjusting for local variations. ElastiGate was validated with biologically relevant datasets in CAR-T cell manufacturing, tumor-infiltrating immunophenotyping, cytotoxicity assays (&gt; 500 data files), and a high-dimensional dataset. Accuracy was evaluated against corresponding manually gated analysis using F1 score statistics. ElastiGate performed similarly to manual gating, with average F1 scores of &gt; 0.9 across all gates. ElastiGate, accessible as a FlowJo Software plugin or in BD FACSuite Software, uses minimal training samples to automate gating while substantially reducing analysis time and outperforming existing 2D plot autogating solutions in F1 scores and ease of implementation.</p>

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Automation of flow cytometry data analysis with elastic image registration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月15日

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  • Allison Irvine,
  • Mohamed Mahmoud Moustafa,
  • Sahul Patel,
  • Aniket Patel,
  • Lilja Hardardottir,
  • Francesca Delvecchio,
  • Taylor Foreman,
  • Jean Oak,
  • Scott J. Bornheimer,
  • Raffaello Cimbro

摘要

Cell populations in flow cytometry are typically identified via visual manual gating, a time-consuming and error-prone approach to select subpopulations based on expression of cellular markers. Batch processing can be used to automate the analysis of bimodally distributed data but underperforms with highly-variable or continuously-expressed markers. We developed a visual pattern recognition automated gating tool, BD ElastiGate Software (hereafter ElastiGate), to recapitulate the visual process of manual gating by automatically adjusting gates to capture local variability. ElastiGate converts histograms and two-dimensional plots into images, then uses elastic B-spline image registration to transform pre-gated training plot images and their gates to corresponding ungated target plot images, thereby adjusting for local variations. ElastiGate was validated with biologically relevant datasets in CAR-T cell manufacturing, tumor-infiltrating immunophenotyping, cytotoxicity assays (> 500 data files), and a high-dimensional dataset. Accuracy was evaluated against corresponding manually gated analysis using F1 score statistics. ElastiGate performed similarly to manual gating, with average F1 scores of > 0.9 across all gates. ElastiGate, accessible as a FlowJo Software plugin or in BD FACSuite Software, uses minimal training samples to automate gating while substantially reducing analysis time and outperforming existing 2D plot autogating solutions in F1 scores and ease of implementation.