自动智能显微镜用于表型识别,空间定位和细胞重定位
Simone Pelicci1, Laura Furia1, Francesco Spadari2,3
1Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy.
Methods in molecular biology (Clifton, N.J.)
|February 2, 2026
概括
癌症异质性对分子特征提出了挑战. 本研究介绍了使用A.M.I.CO.的自动化空间-经济学工作流程. 和激光捕捉微切割 (LCM) 进行精确的,组织上下文意识的分析.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 癌症进展的标志是异质性,包括瘤微环境中的遗传和蛋白质表达变异性.
- 传统的批量测序方法掩盖了空间和单细胞细节,这些细节对于理解瘤复杂性至关重要.
- 新兴的空间分辨和单细胞技术对于研究瘤在其本土组织结构中的研究至关重要.
研究的目的:
- 为了呈现一个自动化,分析驱动的显微镜工作流程,用于空间-omics 分析.
- 为了能够精确地隔离瘤内的不同细胞亚群,同时保持空间上下文.
- 为了促进癌症异质性的全面分子表征.
主要方法:
- 使用高内容成像和AMICO. 图像细胞测量平台用于表型驱动的图像采集.
- 采用激光捕捉微切割 (LCM) 来精确隔离空间定义的区域.
- 在A.M.I.CO.中整合一个空间重新定位程序. 为了确定LCM准的物理坐标.
主要成果:
- 工作流允许在各种样本类型中进行可扩展,高分辨率的图像采集和精确的空间定位.
- 以表型定义的细胞或区域可以准确地定位在样本内进行下游多omics分析.
- 该协议支持智能,分析驱动的获取,用于可复制和组织上下文意识的分析.
结论:
- 这种自动化的空间学工作流提供了一种可重复和可扩展的解决方案,用于研究癌症异质性.
- 图像细胞计和LCM的整合保留了空间上下文,这对于多omics分析至关重要.
- 开发的协议通过实现详细的,组织意识的分子分析来推进生物医学应用.
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