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Updated: Jun 13, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

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Published on: February 23, 2018

Bin2cell从高分辨率Visium HD数据中重建细胞.

Krzysztof Polański^1,2, Raquel Bartolomé-Casado^2,3, Ioannis Sarropoulos^1,2

¹Cambridge Stem Cell Institute and Department of Medicine, University of Cambridge, Cambridge, CB2 0AW, United Kingdom.

Bioinformatics (Oxford, England)

|September 9, 2024

概括

此摘要是机器生成的。

Bin2cell软件从高分辨率的空间转录组学数据中重建单个细胞,改进存档组织样本的分析. 这种方法通过从2微米容器准确地定义细胞边界来增强下游应用.

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Published on: June 5, 2020

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Last Updated: Jun 13, 2025

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科学领域:

空间转录组学空间转录组学
计算生物学是一种计算生物学.
生物信息学是一种生物信息学.

背景情况:

视觉高清提供来自FFPE块的亚细胞分辨率转录组数据.
将捕获区域聚合到单个细胞中提出了分析挑战.

研究的目的:

开发一种计算方法 (Bin2cell) 来从高分辨率的空间转录学数据中重建单个细胞.
通过克服违约捆绑策略的局限性来改进下游分析.

主要方法:

Bin2cell利用形态图像细分和基因表达数据.
重建是从2微米容器进行的,利用亚细胞分辨率.
该软件与现有的Python单细胞和空间转录组学工具兼容.

主要成果:

在没有GPU要求的情况下,Bin2cell在几分钟内高效地重建细胞.
与默认的8微米容器相比,使用重建的细胞进行了改善的下游分析.
在小鼠大脑和人类结直肠癌数据集上进行的验证.

结论:

Bin2cell能够从高分辨率的空间转录组学中准确地重建单细胞.
该方法增强了档案FFPE样本用于转录组分析的实用性.
对于空间生物学研究,Bin2cell提供了一个计算效率高的解决方案.