A temporal extracellular transcriptome atlas of human pre-implantation development
Qiuyang Wu,
Zixu Zhou,
Zhangming Yan,
Megan Connel,
Gabriel Garzo,
Analisa Yeo,
Wei Zhang,
H. Irene Su,
Sheng Zhong
Affiliations
Qiuyang Wu
Shu Chien-Gene Ley Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
Zixu Zhou
Genemo, Inc., San Diego, CA 92130, USA
Zhangming Yan
Shu Chien-Gene Ley Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
Megan Connel
Reproductive Partners San Diego, La Jolla, CA 92037, USA
Gabriel Garzo
Reproductive Partners San Diego, La Jolla, CA 92037, USA
Analisa Yeo
Reproductive Partners San Diego, La Jolla, CA 92037, USA
Wei Zhang
Reproductive Partners San Diego, La Jolla, CA 92037, USA
H. Irene Su
Reproductive Partners San Diego, La Jolla, CA 92037, USA; Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA; Corresponding author
Sheng Zhong
Shu Chien-Gene Ley Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Genemo, Inc., San Diego, CA 92130, USA; Corresponding author
Summary: 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.
