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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Updated: Jun 27, 2025

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

AnnoSpat通过空间蛋白质组学来注释细胞类型并量化细胞排列.

Aanchal Mongia^1,2, Fatema Tuz Zohora^3,4, Noah G Burget^1,2

¹Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Nature communications

|May 3, 2024

概括

AnnoSpat使用神经网络准确地识别组织中的细胞类型和空间模式. 这种工具有助于理解组织组织和疾病进展,如1型糖尿病.

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Last Updated: Jun 27, 2025

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科学领域:

空间生物学空间生物学
计算病理学计算病理学
一个单细胞分析.

背景情况:

细胞组成和空间组织对于器官功能和疾病至关重要.
像图像质量细胞计 (IMC) 和通过索引 (CODEX) 共同检测等空间单细胞蛋白质组测试能够在完整组织中的细胞进行高通量分析.
在精确的细胞类型注释和量化大规模空间数据集中的细胞-细胞邻近性方面仍然存在挑战.

研究的目的:

开发AnnoSpat,这是一款用于细胞类型自动识别和组织空间模式分析的新型计算工具.
为了解决未得到满足的需要,对亚特拉斯尺度空间单细胞蛋白质组数据进行有效分析.
应用AnnoSpat来了解1型糖尿病中的胰腺小岛病理生物学.

主要方法:

开发AnnoSpat,集成神经网络和点处理算法.
应用 AnnoSpat 来分析来自 IMC 和 CODEX 空间蛋白质组测试的数据.
使用AnnoSpat对来自1型糖尿病患者,自身抗体阳性和健康供体队列的人类胰腺数据集.

主要成果:

与现有方法相比,AnnoSpat在快速准确的细胞类型注释方面表现出卓越的性能.
该工具有效量化组织微环境中的细胞-细胞近距离关系.
分析揭示了已知的岛屿病理生物学,并确定了胰腺多 (PP) 细胞和1型糖尿病中CD8+T细胞透的差异动态.

结论:

AnnoSpat提供了一个强大的解决方案,用于在大型空间单细胞蛋白质组数据集中进行自动化的细胞类型注释和空间模式分析.
该工具增强了对组织细胞结构及其在疾病中的作用的理解.
通过描述胰腺小岛内的细胞动态,AnnoSpat促进了对1型糖尿病进展的新见解.