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相关概念视频

Proteomics01:33

Proteomics

7.9K
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...
7.9K

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相关实验视频

Updated: Sep 12, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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单细胞空间蛋白质组学的交叉层次交叉尺度推理和归算.

You Wu1, Lei Xie1,2,3

  • 1Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, USA.

Research square
|August 6, 2025
PubMed
概括
此摘要是机器生成的。

scProSpatial是一个深度学习框架,可以从scRNA-seq数据中重建单细胞空间蛋白质组. 它克服了当前奥米克技术的局限性,使得更广泛的生物学见解成为可能.

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Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level
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Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy
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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.
  • 计算生物学 计算生物学

背景情况:

  • 单细胞和空间奥米克技术已经推进了生物研究,但面临着挑战.
  • 局限性包括批量效应,缺乏多模式测量,蛋白质覆盖范围有限,概括性差.
  • 在单细胞分辨率下空间背景不足,阻碍了对分子驱动因素的理解.

研究的目的:

  • 介绍 scProSpatial,一个统一的深度学习框架.
  • 从scRNA-seq.中推断和推算高保真单细胞空间蛋白质组.
  • 解决当前实验欧米克学方法的局限性.

主要方法:

  • 开发了scProSpatial,这是一个多模式,多规模的深度学习框架.
  • 框架从单细胞RNA测序 (scRNA-seq) 数据中推断和赋值空间蛋白质组学.
  • 使用了全面的评估和转移性乳腺癌的案例研究.

主要成果:

  • scProSpatial准确地预测了空间蛋白质丰度,而没有共享的转录组学特征.
  • 与现有方法相比,蛋白质覆盖范围扩大了50倍.
  • 证明了对分布之外的场景进行强有力的概括.

结论:

  • scProSpatial有效地克服了单细胞空间蛋白质组学的关键挑战.
  • 该框架有助于跨层面和跨规模的多学科融合.
  • 能够更深入地了解复杂的生物系统,如转移性乳腺癌.