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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: May 8, 2026

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

stTrace:从空间转录组中检测时空领域,以追踪发育路径.

Zhangdi Song^1,2, Changyu Zheng¹, Jiaxing Chen¹

¹Guangdong Provincial/Zhuhai Key Laboratory IRADS, Beijing Normal-Hong Kong Baptist University, 2000 Jintong Road, Tangjiawan, Zhuhai 519087, Guangdong Province, China.

Briefings in bioinformatics

|November 16, 2025

概括

此摘要是机器生成的。

我们开发了stTrace,一种使用空间转录学绘制组织细胞发育图的新算法. 它揭示了时空领域和发育路径,改善了对生物体生长和疾病进展的理解.

关键词:

发展发展发展发展发展.空间转录组空间转录组时间空间领域的空间-时间领域.飞行轨道的轨迹是什么

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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相关实验视频

Last Updated: May 8, 2026

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

科学领域:

发展生物学发展生物学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

研究生物的发育对于理解复杂的生物系统至关重要.
目前使用单细胞基因表达数据的方法忽略了空间细胞相互作用.
现有的空间转录算法主要识别区域,而不是发展连接.

研究的目的:

介绍stTrace,用于检测时空域的算法,使用空间转录学来追踪发育路径.
整合细胞发育,基因表达和空间位置,进行全面的发育分析.
克服目前捕捉组织发育的方法的局限性.

主要方法:

开发了stTrace算法,集成空间转录学数据.
嵌入的细胞发育程度,基因表达和空间位置.
确定了代表具有相似功能和发育阶段的细胞的"时空域".
分析了领域之间的等级关系,以推断发展连接.

主要成果:

与老鼠胚胎和人类乳腺癌数据集的传统算法相比,stTrace实现了更高的分辨率和发育一致性.
在小鼠大脑和眼睛组织中确定了不同的时空领域,具有显著的基因表达差异.
重建了人类癌症数据的发育树,推断出癌细胞传播方向.

结论:

stTrace通过识别时空领域,有效地绘制组织中的发育轨迹.
该算法为生物体发育和疾病进展提供了新的见解,例如癌细胞迁移.
这种方法增强了空间转录学数据的分析,以了解复杂的生物过程.