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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

空间平滑量化改进了成像质细胞计数据集中的细胞类型.

Reto Gerber¹, Jake Griner², Daniel Incicau¹

¹Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich.

bioRxiv : the preprint server for biology

|December 25, 2025

概括

在成像质细胞计 (IMC) 中准确的细胞类型注释依赖于预处理. 简单的空间平滑或细胞面膜重新采样方法通过纠正信号溢出和增强标记聚合来显著改善IMC细胞注释.

关键词:

细胞类型的注释.图像质量细胞计图像质量细胞计标记器聚合标记器聚合

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Last Updated: Jan 7, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

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Published on: June 30, 2017

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科学领域:

单细胞生物学单细胞生物学
计算病理学计算病理学
生物医学成像分析分析

背景情况:

在成像质细胞计 (IMC) 中准确的细胞类型注释对于生物学洞察至关重要.
规范化,细分和标记聚合等预处理步骤对于IMC数据分析至关重要.
由于空间分辨率有限和细胞边界不确定,信号溢出会扭曲标记器强度,导致错误的注释.

研究的目的:

系统地研究空间分辨率和细分变异性对IMC中每细胞标记器聚合的影响.
分析IMC大规模研究中的技术偏差,并评估规范化策略.
为提高IMC细胞类型注释的溢出纠正策略进行基准测试.

主要方法:

利用模拟的IMC数据集来评估空间分辨率和细分对标记聚合的影响.
在大型IMC研究中分析了技术偏差和规范化有效性.
在模拟和真实IMC数据集上对各种溢出纠正方法进行了基准测试.

主要成果:

基于空间分辨率和细分的细胞类型注释中可靠标记分离的上限.
证明适当的规范化在大规模的IMC研究中减少了批量效应,而不会失去生物变异性.
确定了两种简单,有效的溢出纠正方法:使用平均总和进行空间平滑或使用中位数计算进行细胞面膜重新抽样,表现优于基线平均总和.

结论:

空间分辨率,规范化和标记集成是准确的IMC单细胞注释的关键预处理步骤.
简单的预处理策略,如空间平滑或细胞面膜重新采样,可以显著提高IMC数据分析.
有效的溢出纠正对于在IMC中强大的细胞类型识别至关重要.