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ABSTRACT The complex process by which a single-celled zygote develops into a viable embryo is nothing short of a miraculous wonder of the natural world. Elucidating how this process is orchestrated in humans has long eluded the grasp of scientists due to ethical and practical limitations. Thankfully, pluripotent stem cells that resemble early developmental cell types possess the ability to mimic specific embryonic events. As such, murine and human stem cells have been leveraged by scientists to create in vitro models that aim to recapitulate different stages of early mammalian development. Here, we examine the wide variety of stem cell-based embryo models that have been developed to recapitulate and study embryonic events, from pre-implantation development through to early organogenesis. We discuss the applications of these models, key considerations regarding their importance within the field, and how such models are expected to grow and evolve to achieve exciting new milestones in the future.
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Machine learning-assisted imaging analysis of a human epiblast model
Abstract The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell–cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from a human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell–cell and cell–environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.
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- PAR ID:
- 10324410
- Date Published:
- Journal Name:
- Integrative Biology
- Volume:
- 13
- Issue:
- 9
- ISSN:
- 1757-9708
- Page Range / eLocation ID:
- 221 to 229
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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SUMMARY Emerging human pluripotent stem cell (hPSC)-based embryo models are useful for studying human embryogenesis. Particularly, there are hPSC-based somitogenesis models using free-floating culture that recapitulate somite formation. Somitogenesisin vivoinvolves intricately orchestrated bio-chemical and -mechanical events. However, none of the current somitogenesis models controls biochemical gradients or biomechanical signals in the culture, limiting their applicability to untangle complex biochemical-biomechanical interactions that drive somitogenesis. Here we report a new human somitogenesis model by confining hPSC-derived presomitic mesoderm (PSM) tissues in microfabricated trenches. Exogenous microfluidic morphogen gradients imposed on PSM cause axial patterning and trigger spontaneous rostral-to-caudal somite formation. A mechanical theory is developed to explain the size dependency between somites and PSM. The microfluidic somitogenesis model is further exploited to reveal regulatory roles of cellular and tissue biomechanics in somite formation. This study presents a useful microengineered, hPSC-based model for understanding the bio-chemical and -mechanical events that guide somite formation.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/intbio/zyab014
