Dynamic Proteome Landscape During Preimplantation Human Embryo Development and Trophectoderm Stem Cell-Differentiation

IF 3.9 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2025-08-01 DOI:10.1002/pmic.70017
Alin Rai, Qi Hui Poh, Hiroaki Okae, Takahiro Arima, Mehdi Totonchi, David W. Greening
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引用次数: 0

Abstract

Embryo development involves fertilization of a mature ovum, which, after sequential cell divisions (2-,4-8-cells and morula), undergoes differentiation into implantation competent blastocyst. The blastocyst comprises of inner cell mass surrounded by an outer layer of cells called the trophoblast (TSblast) that, upon attachment to the endometrium, differentiates into extravillous trophoblasts (EVTs) that facilitates embryo invasion into the endometrium for intrauterine embryo development, and syncytiotrophoblast (ST) that form the placenta. Such cellular differentiation stages are critical for embryogenesis and implantation, although the protein expression landscape remains poorly understood in humans. Using quantitative mass spectrometry analysis, we systematically monitored the protein expression landscape and their dynamic regulation between human ovum (M2), 8-cell embryo, and blastocysts stages, and trophoblast lineage-specific differentiation into EVTs and ST. Proteins temporally regulated from M2-8 cell-blastocyst stage displayed significant enrichment for metabolic protein networks. We specifically identified 156 proteins associated with 8-cell embryos to blastocyst development, 54 displayed similar correlation at the transcriptomic level including mitochondrial, junction/secretory granule-associated proteins that carry glycolytic, antioxidant, and telomerase maintenance functions. We reveal a striking lineage-specific reprogramming of TSblast proteome during fate-specification. These findings extend our knowledge of the sequential order of protein landscape reprogramming and processes during early human embryogenesis and trophoblast function.

Summary

  • Although genomic and transcriptomic studies have provided key understanding of the genetic programs underlying preimplantation embryo development, the protein expression landscape remains unexplored. Here, a quantitative proteomic study of human preimplantation embryo stages reveal a dynamic proteome landscape from M2, 8-cell, and blastocyst stage, and during trophoblast stem cell (TS) differentiation.
  • Identified key factors in early human embryos and lineage-specific trophoblast proteome profiles, further correlated with transcriptomic analyses.
  • This direct proteomic analysis provides a quantitative and temporal analysis of the dynamic protein expression in human embryos during preimplantation development and a powerful resource to enable further mechanistic studies on human trophoblast development and function.

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着床前人类胚胎发育和滋养外胚层干细胞分化过程中的动态蛋白质组景观。
胚胎发育包括成熟卵子的受精,成熟卵子经过连续的细胞分裂(2-、4-8细胞和桑葚胚),分化为具有着床能力的囊胚。囊胚包括由一层被称为滋养细胞(TSblast)的外层细胞包围的内细胞团,滋养细胞附着在子宫内膜上后,分化为促进胚胎侵入子宫内膜进行宫内胚胎发育的外滋养细胞(EVTs)和形成胎盘的合胞滋养细胞(ST)。这种细胞分化阶段对胚胎发生和着床至关重要,尽管人类对蛋白质表达的了解仍然很少。通过定量质谱分析,我们系统地监测了人卵(M2)、8细胞胚胎和囊胚阶段的蛋白质表达格局及其动态调控,以及滋养细胞向evt和st的特异性分化,从M2-8细胞囊胚阶段暂时调节的蛋白质在代谢蛋白网络中表现出显著的富集。我们特别鉴定了156种与8细胞胚胎到囊胚发育相关的蛋白,其中54种在转录组水平上表现出类似的相关性,包括线粒体、连接/分泌颗粒相关蛋白,这些蛋白携带糖酵解、抗氧化和端粒酶维持功能。我们揭示了一个惊人的谱系特异性重编程TSblast蛋白质组在命运规范。这些发现扩展了我们对早期人类胚胎发生和滋养细胞功能过程中蛋白质景观重编程的顺序和过程的认识。摘要:尽管基因组学和转录组学研究已经为着床前胚胎发育的遗传程序提供了关键的理解,但蛋白质表达景观仍未被探索。在这里,人类着床前胚胎阶段的定量蛋白质组学研究揭示了M2、8细胞和囊胚阶段以及滋养细胞干细胞(TS)分化过程中的动态蛋白质组学景观。确定了早期人类胚胎的关键因素和谱系特异性滋养细胞蛋白质组谱,并进一步与转录组分析相关。这种直接的蛋白质组学分析提供了人类胚胎着床前发育过程中动态蛋白质表达的定量和时间分析,为进一步研究人类滋养细胞发育和功能的机制提供了有力的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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