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Updated: Jul 15, 2025

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

优化:一个优化成像质量细胞计分析框架,用于对比细分和数据探索.

Bethany Hunter^1,2, Ioana Nicorescu³, Emma Foster⁴

¹Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.

Cytometry. Part A : the journal of the International Society for Analytical Cytology

|September 26, 2023

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此摘要是机器生成的。

OPTIMAL框架通过优化细胞细分和数据探索来改善成像质量细胞计 (IMC) 分析,从而导致更准确的细胞识别和空间分析. 这种标准化的方法提高了多重组织成像数据的可靠性.

科学领域:

生物医学成像技术生物医学成像技术
计算生物学计算生物学
免疫学免疫学免疫学

背景情况:

多复合组织成像,包括成像质细胞计 (IMC),在单细胞细分和数据分析方面面临挑战.
与单细胞悬浮数据相比,低于最佳的分析可能导致不准确的细胞表型,状态和空间关系识别.

研究的目的:

开发和验证"优化成像质量细胞计分析 (OPTIMAL) "框架.
为了对IMC数据分析的各种方法进行基准测试,包括细分,参数转换,批次校正,可视化,聚类和空间邻近分析.

主要方法:

测试了多个细胞细分模型,arcsinh辅因子值,缩小维度算法 (如PacMap) 和聚类方法 (如FLOWSOM).
使用了FFPE人类桃体组织微阵列,在12个批次中染色了27个金属标记抗体.
评估了邻近分析技术,包括"磁盘"像素扩展与"边界盒"方法.

主要成果:

来自Ilastik的概率图改进了单细胞细分,尽管聚类后的问题更明显.
一个1的arcsinh辅因子和Z-score规范化有效地转换了参数,并纠正了批量效应.
在缩小维度方面,PacMap表现出色,而在细胞类型识别方面,FLOWSOM的表现优于Phenograph.

关键词:

图像分析图像分析图像细胞测量图像细胞测量图像成像质量细胞计量.组织细分组织细分

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"磁盘"像素扩展在邻近分析中优越,对象过至关重要.

结论:

OPTIMAL框架提供了一种标准化的方法来评估和整合IMC数据分析管道.
它在多重组织成像中促进了准确的细胞识别,状态确定和空间分析.
生成的 .FCS 文件可以使用熟悉的流细胞计软件和算法进行单细胞探索.