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

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Published on: July 6, 2022

通过DBiTplus将基于成像和基于测序的空间Omics映射集成到相同的组织部分,通过DBiTplus进行映射.

Archibald Enninful¹, Zhaojun Zhang², Dmytro Klymyshyn³

¹Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.

Research square

|December 23, 2024

概括

组织测序加 (DBiTplus) 中的确定性条形编码集成了空间转录组学和蛋白质分析. 这种方法使单细胞分辨率的空间图谱和细胞对细胞的途径探索成为可能.

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科学领域:

基因组学就是基因组学.
蛋白质组学是指蛋白质组学.
系统生物学系统生物学
生物信息学是一种生物信息学.

背景情况:

了解细胞异质性和功能需要同时绘制组织内的基因和蛋白质表达的地图.
当前的空间奥米克技术往往缺乏用于全面的细胞类型识别和通路分析所需的分辨率或多模式集成.

研究的目的:

在组织测序加 (DBiTplus) 中引入确定性条形码,这是一个整合性的多模态空间奥米克方法.
通过结合空间转录组学和空间蛋白质分析在同一组织部分上,使单细胞分辨率细胞类型和生物通路的基因组规模查询成为可能.

主要方法:

DBiTplus使用现场逆转录用于cDNA合成,微流体输送DNA寡头用于空间条形码,并采用RNaseH介导检索条形码cDNA.
完整的组织部分被保存用于高复合蛋白成像,使用CODEX (CO-Detection by INdexing),一种循环免疫光技术.
开发了计算管道,以记录和整合来自空间转录组学 (DBiT-seq) 和空间蛋白组学 (CODEX) 模式的数据.

主要成果:

DBiTplus成功地将空间转录组学和蛋白质分析数据整合到同一组织部分.
在空间转录组斑点内实现了精确的细胞类型,使单细胞解析空间转录组图谱的图像引导分解成为可能.
该方法在小鼠胚胎,人类淋巴结和人类淋巴瘤FFPE组织上得到了验证,证明了其在不同生物背景和样本类型中的实用性.

结论:

DBiTplusCODEX为整合性实验程序和计算创新提供了统一的工作流.
这种方法有助于在全基因组范围内进行空间分辨的单细胞图谱绘制和细胞对细胞的生物学途径探索.
在复杂的组织架构中,DBiTplus显著提高了将细胞身份与功能连接起来的能力.