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      Human naive epiblast cells possess unrestricted lineage potential

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          Summary

          Classic embryological experiments have established that the early mouse embryo develops via sequential lineage bifurcations. The first segregated lineage is the trophectoderm, essential for blastocyst formation. Mouse naive epiblast and derivative embryonic stem cells are restricted accordingly from producing trophectoderm. Here we show, in contrast, that human naive embryonic stem cells readily make blastocyst trophectoderm and descendant trophoblast cell types. Trophectoderm was induced rapidly and efficiently by inhibition of ERK/mitogen-activated protein kinase (MAPK) and Nodal signaling. Transcriptome comparison with the human embryo substantiated direct formation of trophectoderm with subsequent differentiation into syncytiotrophoblast, cytotrophoblast, and downstream trophoblast stem cells. During pluripotency progression lineage potential switches from trophectoderm to amnion. Live-cell tracking revealed that epiblast cells in the human blastocyst are also able to produce trophectoderm. Thus, the paradigm of developmental specification coupled to lineage restriction does not apply to humans. Instead, epiblast plasticity and the potential for blastocyst regeneration are retained until implantation.

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          Highlights

          • Human naive pluripotent stem cells form blastocyst trophectoderm directly

          • Unlike in mouse, human naive epiblast regenerates trophectoderm

          • Inhibition of ERK and Nodal drives trophectoderm differentiation

          • Potency changes from trophectoderm to amnion during pluripotency progression

          Abstract

          Human pluripotent stem cells (hPSCs) exist in naive or primed states, but it has been unclear whether these correspond to distinct developmental potencies. Here, Guo et al. show that naive hPSCs differentiate into trophectoderm, the founder tissue of the placenta, whereas primed hPSCs have lost this potential and instead form the amnion.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                Journal
                Cell Stem Cell
                Cell Stem Cell
                Cell Stem Cell
                Cell Press
                1934-5909
                1875-9777
                03 June 2021
                03 June 2021
                : 28
                : 6
                : 1040-1056.e6
                Affiliations
                [1 ]Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
                [2 ]Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 1GA, UK
                [3 ]Department of Biochemistry, University of Cambridge, Cambridge CB2 1QR, UK
                [4 ]Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK
                [5 ]Guangzhou Institutes of Biomedicine and Health (GIBH), Chinese Academy of Sciences, Guangzhou 510530, China
                Author notes
                []Corresponding author g.guo@ 123456exeter.ac.uk
                [∗∗ ]Corresponding author jn270@ 123456cscr.cam.ac.uk
                [∗∗∗ ]Corresponding author austin.smith@ 123456exeter.ac.uk
                [6]

                Present address: Key Laboratory of Arrhythmias, Ministry of Education, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China

                [7]

                These authors contributed equally

                [8]

                Lead contact

                Article
                S1934-5909(21)00076-X
                10.1016/j.stem.2021.02.025
                8189439
                33831366
                108939ba-c7bf-4278-a58b-bb12078c0478
                © 2021 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 January 2020
                : 17 September 2020
                : 23 February 2021
                Categories
                Article

                Molecular medicine
                pluripotency, epiblast, embryonic stem cells, trophoblast, embryo, blastocyst, lineage segregation, differentiation

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