Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
Nat Commun. 2024 Jun 15;15(1):5135. doi: 10.1038/s41467-024-48870-5.
The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights.
多指标成像的规模和维度不断扩大，这就需要可重复、全面且用户友好的计算流程。TRACERx-PHLEX 作为三个独立但可互操作的模块，执行基于深度学习的细胞分割（deep-imcyto）、自动细胞类型注释（TYPEx）和可解释的空间分析（Spatial-PHLEX）。PHLEX 生成单细胞身份、组织隔室中细胞密度、标记物阳性调用以及细胞屏障评分等空间指标，同时生成摘要图和空间可视化。PHLEX 是在 TRACERx 研究中使用成像质谱细胞术（IMC）开发的，使用已发表的 Co-detection by indexing (CODEX)、IMC 和正交数据进行了验证，并与最先进的方法进行了基准测试。我们评估了它在不同组织类型、组织固定条件、图像大小和抗体面板上的使用情况。由于 PHLEX 是一个自动化和容器化的 Nextflow 流程，因此不需要手动评估、编程技能或病理学专业知识。PHLEX 在高度多指标数据不断增长的领域提供了端到端的解决方案，并提供了临床相关的见解。
