A temporal extracellular transcriptome atlas of human pre-implantation development.
A temporal extracellular transcriptome atlas of human pre-implantation development.
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DOI：
10.1016/j.xgen.2023.100464
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发表时间：
2024-01-10
期刊：
影响因子：
--
通讯作者：
Zhong, Sheng
中科院分区：
文献类型：
--
作者：
Wu, Qiuyang;Zhou, Zixu;Yan, Zhangming;Connel, Megan;Garzo, Gabriel;Yeo, Analisa;Zhang, Wei;Su, H. Irene;Zhong, Sheng
非侵入性评估人类植入前胚胎中的基因表达产物仍然是一个重大挑战。在这里，我们开发了一种非侵入性的方法，用于全面表征细胞外RNA（exRNA）在一个单一的液滴的用过的媒体，用于培养人类体外受精胚胎。我们生成了人类植入前发育的时间细胞外转录组图谱（TETA）。TETA由五个发育阶段的245个exRNA测序数据集组成。这些数据揭示了每个阶段大约4，000个exRNA。发育停滞胚胎的exRNA富含参与细胞周期负调控的基因，揭示了发育停滞的exRNA特征。此外，机器学习模型可以基于exRNA水平近似基于形态学的胚胎质量评级。这些数据揭示了编码基因衍生的exRNA在人类植入前发育的每个阶段的广泛存在，并且这些exRNA提供了关于胚胎生理学的丰富信息。一种对IVF胚胎培养基中exRNA进行测序的非侵入性方法人类植入前发育exRNA组的TETA与发育停滞和胚胎质量相关基于exRNA水平评估胚胎质量的机器学习模型IVF人类胚胎基因表达的非侵入性表征可以提供对胚胎的分子水平理解。Wu等人对胚胎培养基中的exRNA进行测序，并开发机器学习模型，以根据exRNA图谱预测胚胎质量。
Non-invasively evaluating gene expression products in human pre-implantation embryos remains a significant challenge. Here, we develop a non-invasive method for comprehensive characterization of the extracellular RNAs (exRNAs) in a single droplet of spent media that was used to culture human in vitro fertilization embryos. We generate the temporal extracellular transcriptome atlas (TETA) of human pre-implantation development. TETA consists of 245 exRNA sequencing datasets for five developmental stages. These data reveal approximately 4,000 exRNAs at each stage. The exRNAs of the developmentally arrested embryos are enriched with the genes involved in negative regulation of the cell cycle, revealing an exRNA signature of developmental arrest. Furthermore, a machine-learning model can approximate the morphology-based rating of embryo quality based on the exRNA levels. These data reveal the widespread presence of coding gene-derived exRNAs at every stage of human pre-implantation development, and these exRNAs provide rich information on the physiology of the embryo. A non-invasive method for sequencing exRNA from IVF embryo culture media A TETA of human pre-implantation development exRNA panels correlated with developmental arrest and embryo quality A machine-learning model to evaluate embryo quality based on exRNA levels Non-invasive characterization of gene expression from IVF human embryos can provide a molecular-level understanding of the embryo. Wu et al. sequence the exRNAs in the embryo culture media and develop a machine-learning model to predict embryo quality based on exRNA profiles.
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