Blastocyst-like structures generated from human pluripotent stem cells
TL;DR: In this article, the authors developed an effective three-dimensional culture strategy with successive lineage differentiation and self-organization to generate blastocyst-like structures in vitro, which they termed human blastoids.
Abstract: Limited access to embryos has hampered the study of human embryogenesis and disorders that occur during early pregnancy. Human pluripotent stem cells provide an alternative means to study human development in a dish1–7. Recent advances in partial embryo models derived from human pluripotent stem cells have enabled human development to be examined at early post-implantation stages8–14. However, models of the pre-implantation human blastocyst are lacking. Starting from naive human pluripotent stem cells, here we developed an effective three-dimensional culture strategy with successive lineage differentiation and self-organization to generate blastocyst-like structures in vitro. These structures—which we term ‘human blastoids’—resemble human blastocysts in terms of their morphology, size, cell number, and composition and allocation of different cell lineages. Single-cell RNA-sequencing analyses also reveal the transcriptomic similarity of blastoids to blastocysts. Human blastoids are amenable to embryonic and extra-embryonic stem cell derivation and can further develop into peri-implantation embryo-like structures in vitro. Using chemical perturbations, we show that specific isozymes of protein kinase C have a critical function in the formation of the blastoid cavity. Human blastoids provide a readily accessible, scalable, versatile and perturbable alternative to blastocysts for studying early human development, understanding early pregnancy loss and gaining insights into early developmental defects. An in vitro culture strategy enables the generation of blastocyst-like structures termed human blastoids from naive human pluripotent stem cells, providing a model for studying human embryogenesis.
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TL;DR: In this paper, the authors describe a human embryo model (blastoid) generated by self-organization, which is composed of three tissue layers displaying exclusive lineage markers, mimicking the natural blastocyst.
116 citations
Newcastle University1, Wellcome Trust Sanger Institute2, University of Pennsylvania3, Children's Hospital of Philadelphia4, Karolinska Institutet5, Cincinnati Children's Hospital Medical Center6, University of Toronto7, University of Cambridge8, University of Basel9, Vision Institute10, UCL Institute of Child Health11, Aix-Marseille University12, university of lille13, Zhejiang University14, Broad Institute15, McGill University16, University of California, San Francisco17, Royal Institute of Technology18, Francis Crick Institute19, Science for Life Laboratory20, Memorial Sloan Kettering Cancer Center21, Genentech22, Massachusetts Institute of Technology23, New York University24, ETH Zurich25
TL;DR: The Human Developmental Cell Atlas (HDCA) project as discussed by the authors aims to create a comprehensive reference map of cells during development by mapping and modelling human development using state-of-the-art technologies.
Abstract: The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development. This Perspective outlines the Human Developmental Cell Atlas initiative, which uses state-of-the-art technologies to map and model human development across gestation, and discusses the early milestones that have been achieved.
83 citations
TL;DR: In this article, expanded pluripotent stem cells (EPSCs) are used to generate self-organizing cystic structures from human EPSCs that display some hallmarks of human early embryogenesis.
Abstract: Understanding human development is of fundamental biological and clinical importance. Despite its significance, mechanisms behind human embryogenesis remain largely unknown. Here, we attempt to model human early embryo development with expanded pluripotent stem cells (EPSCs) in 3-dimensions. We define a protocol that allows us to generate self-organizing cystic structures from human EPSCs that display some hallmarks of human early embryogenesis. These structures mimic polarization and cavitation characteristic of pre-implantation development leading to blastocyst morphology formation and the transition to post-implantation-like organization upon extended culture. Single-cell RNA sequencing of these structures reveals subsets of cells bearing some resemblance to epiblast, hypoblast and trophectoderm lineages. Nevertheless, significant divergences from natural blastocysts persist in some key markers, and signalling pathways point towards ways in which morphology and transcriptional-level cell identities may diverge in stem cell models of the embryo. Thus, this stem cell platform provides insights into the design of stem cell models of embryogenesis. Human early development remains largely inaccessible, owing to technical and ethical limitations of working with natural embryos. Here the authors assess the extent to which human expanded pluripotent stem cells can specify distinct cell lineages and capture aspects of early human embryogenesis.
77 citations
TL;DR: In this article, a three-dimensional (3D) and two-step induction protocol for generating blastocyst-like structures from human extended pluripotent stem (EPS) cells was developed.
Abstract: Human blastocysts are comprised of the first three cell lineages of the embryo: trophectoderm, epiblast and primitive endoderm, all of which are essential for early development and organ formation. However, due to ethical concerns and restricted access to human blastocysts, a comprehensive understanding of early human embryogenesis is still lacking. To bridge this knowledge gap, a reliable model system that recapitulates early stages of human embryogenesis is needed. Here we developed a three-dimensional (3D), two-step induction protocol for generating blastocyst-like structures (EPS-blastoids) from human extended pluripotent stem (EPS) cells. Morphological and single-cell transcriptomic analyses revealed that EPS-blastoids contain key cell lineages and are transcriptionally similar to human blastocysts. Furthermore, EPS-blastoids are similar with human embryos that were cultured for 8 or 10 days in vitro, in terms of embryonic structures, cell lineages and transcriptomic profiles. In conclusion, we developed a scalable system to mimic human blastocyst development, which can potentially facilitate the study of early implantation failure that induced by developmental defects at early stage.
65 citations
TL;DR: In this paper, the authors re-evaluated the blastoid data with a more comprehensive cellular reference, including extended cultures of blastocysts, several stem cell-based embryo models and a gastrulation stage human specimen, and they concluded that reprogrammed blastoids by Liu et al. fail to generate cells with trophectoderm profiles.
Abstract: Two recent papers in Nature show that human blastocyst-like structures (or blastoids) can be generated from human pluripotent stem cells1 or through reprogramming of fibroblasts2, respectively. Both papers perform extensive single cell transcriptional analysis and compare blastoid cells with the cells in preimplantation human embryos3,4, leading to a conclusion that the blastoids contain cell lineages corresponding to the epiblast, primitive endoderm and trophectoderm in preimplantation human embryos. Transcriptional analysis is, however, critically dependent on having relevant reference samples, not only of targeted cell types but also of potential alternative cell lineages. For this reason, we have reevaluated the blastoid data with a more comprehensive cellular reference, including extended cultures of blastocysts, several stem cell-based embryo models and a gastrulation stage human specimen. From this reanalysis we resolve that reprogrammed blastoids by Liu et al. fail to generate cells with trophectoderm profiles. Instead, cells identified as trophectoderm lineages in reprogrammed blastoids possess a transcriptional profile more representative of amniotic cells in post-implantation human embryos. Our reanalysis also shows that stem cell-derived blastoids1 did contain trophectoderm-like cells, highlighting the potential of human blastoids to model blastocyst development.
50 citations
References
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TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
47,974 citations
TL;DR: 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 outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation 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. Results 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. Availability and implementation 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/.
30,684 citations
TL;DR: It is demonstrated that iPS cells can be generated from adult human fibroblasts with the same four factors: Oct3/4, Sox2, Klf4, and c-Myc.
18,175 citations
TL;DR: Human blastocyst-derived, pluripotent cell lines are described that have normal karyotypes, express high levels of telomerase activity, and express cell surface markers that characterize primate embryonic stem cells but do not characterize other early lineages.
Abstract: Human blastocyst-derived, pluripotent cell lines are described that have normal karyotypes, express high levels of telomerase activity, and express cell surface markers that characterize primate embryonic stem cells but do not characterize other early lineages. After undifferentiated proliferation in vitro for 4 to 5 months, these cells still maintained the developmental potential to form trophoblast and derivatives of all three embryonic germ layers, including gut epithelium (endoderm); cartilage, bone, smooth muscle, and striated muscle (mesoderm); and neural epithelium, embryonic ganglia, and stratified squamous epithelium (ectoderm). These cell lines should be useful in human developmental biology, drug discovery, and transplantation medicine.
15,555 citations
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Abstract: MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
14,103 citations