Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos

By S. Petropoulos, D. Edsgärd, Björn Reinius, Q. Deng, S. Panula, Simone Codeluppi, Álvaro Plaza Reyes, S. Linnarsson, Rickard Sandberg, F. Lanner. Cell, 2016

997 citations recorded by the source corpus.

Summary Mouse studies have been instrumental in forming our current understanding of early cell-lineage decisions; however, similar insights into the early human development are severely limited. Here, we present a comprehensive transcriptional map of human embryo development, including the sequenced transcriptomes of 1,529 individual cells from 88 human preimplantation embryos. These data show that cells undergo an intermediate state of co-expression of lineage-specific genes, followed by a concurrent establishment of the trophectoderm, epiblast, and primitive endoderm lineages, which coincide with blastocyst formation. Female cells of all three lineages achieve dosage compensation of X chromosome RNA levels prior to implantation. However, in contrast to the mouse, XIST is transcribed from both alleles throughout the progression of this expression dampening, and X chromosome genes maintain biallelic expression while dosage compensation proceeds. We envision broad utility of this transcriptional atlas in future studies on human development as well as in stem cell research.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b34bc4b6cccd0655a28dfd933ba30c54b1b42006

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Citations in the corpus

Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “In addition, scRNA-seq is increasingly being utilized to delineate cell lineage relationships in early development 15 , myoblast differentiation 16 , and lymphocyte fate determination 17 .”
Proliferation, survival and metabolism: the role of PI3K/AKT/mTOR signalling in pluripotency and cell fate determination (2016, background): “…in vitro for extended periods beyond implantation (Deglincerti et al., 2016; Shahbazi et al., 2016) and single-cell gene expression profiling (Petropoulos et al., 2016; Yan et al., 2013) represent exciting avenues through which we may further improve defined culture conditions and explore…”
Single-cell mRNA quantification and differential analysis with Census (2017, methodology): “We assessed Census’ accuracy by reanalyzing several experiments that included spike-in controls 4,10–15 (accession codes for all data we analyzed are available in Online Methods).”
Landscape of X chromosome inactivation across human tissues (2016, positive): “1), as demonstrated recently in mouse studies 19-22 , and in human fibroblasts 23 and preimplantation development 24 .”
Identification of spatial expression trends in single-cell gene expression data (2018, positive): “Methods have also been developed to assign cells along continuous processes12– 15, e.g. along a pseudo-time of development15 or pseudo-space to capture niches16.”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, background): “…inference with and without imputing the dropout events on three published temporal scRNA-seq datasets, mouse preimplantation embryonic development data (Deng [6]), human preimplantation embryonic development data (Pet-ropoulos [38]), and mouse early mesodermal development data (Scialdone [37]).”