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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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相关实验视频

Updated: May 7, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

使用变压器对单细胞转录组学和蛋白组学进行联合分析.

Yuanyuan Chen¹, Xiaodan Fan², Chaowen Shi³

¹School of Mathematical Science, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.

NPJ systems biology and applications

|January 1, 2025

概括

此摘要是机器生成的。

我们开发了scTEL,这是一种深度学习工具,可以从单细胞RNA测序数据中预测蛋白质表达,从而降低CITE-seq成本. 这种计算方法可以实现成本效益高的蛋白质分析,并集成各种数据集.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

相关实验视频

Last Updated: May 7, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

科学领域:

单细胞生物学单细胞生物学
计算生物学是一种计算生物学.
分子生物学分子生物学

背景情况:

CITE-seq同时测量单细胞水平上的RNA和蛋白质表达,这对于理解细胞异质性至关重要.
CITE-seq的高实验成本限制了其广泛的应用.
对RNA和蛋白质表达的综合分析对于深入的生物学见解至关重要.

研究的目的:

开发一种计算方法,scTEL,用于从单细胞RNA测序 (scRNA-seq) 数据中预测蛋白质表达.
为了降低与蛋白质表达分析相关的实验成本.
创建一个统一的框架,用于集成多个CITE-seq数据集与不同的蛋白质面板.

主要方法:

scTEL使用基于变压器编码器层的深度学习框架.
该模型建立了从测量的RNA表达到同一细胞内的未观察到的蛋白质表达的映射.
建议建立一个统一的框架来整合多个CITE-seq数据集,处理蛋白质面板中的部分重叠.

主要成果:

scTEL可以准确地从成本效益低的scRNA-seq数据中预测蛋白质表达.
计算方法显著降低了蛋白质表达分析的成本.
该模型在公开CITE-seq数据集的经验验验证方面表现优于现有方法.
scTEL有效地将多个CITE-seq数据集与异质蛋白质面板集成在一起.

结论:

scTEL提供了一种具有成本效益的计算解决方案,用于从scRNA-seq数据中推断蛋白质表达.
该框架促进了多种CITE-seq数据集的整合,增强了多组单细胞分析.
这种方法通过减少实验障碍,使高分辨率的细胞概况民主化.