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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Acoustic Actuation of Integrin‐Bound Microbubbles for Mechanical Phenotyping during Differentiation and Morphogenesis of Human Embryonic Stem Cells
Early human embryogenesis is a dynamic developmental process, involving continuous and concomitant changes in gene expression, structural reorganization, and cellular mechanics. However, the lack of investigation methods has limited the understanding of how cellular mechanical properties change during early human embryogenesis. In this study, ultrasound actuation of functionalized microbubbles targeted to integrin (acoustic tweezing cytometry, ATC) is employed for in situ measurement of cell stiffness during human embryonic stem cell (hESC) differentiation and morphogenesis. Cell stiffness, which is regulated by cytoskeleton structure, remains unchanged in undifferentiated hESCs, but significantly increases during neural differentiation. Further, using the recently established in vitro 3D embryogenesis models, ATC measurements reveal that cells continue to stiffen while maintaining pluripotency during epiblast cyst formation. In contrast, during amniotic cyst formation, cells first become stiffer during luminal cavity formation, but softens significantly when cells differentiate to form amniotic cysts. These results suggest that cell stiffness changes not only due to 3D spatial organization, but also with cell fate change. ATC therefore provides a versatile platform for in situ measurement of cellular mechanical property, and cell stiffness may be used as a mechanical biomarker for cell lineage diversification and cell fate specification during embryogenesis.
more » « less- NSF-PAR ID:
- 10079532
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Small
- Volume:
- 14
- Issue:
- 50
- ISSN:
- 1613-6810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT The soil bacterium
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Myxococcus xanthus forms biofilms in response to starvation, marked by cells aggregating into mounds. Some aggregates persist as spore-filled fruiting bodies, while others disperse after initial formation for unknown reasons. Here, we use a combination of cell tracking analysis and computational simulations to identify behaviors at the cellular level that contribute to aggregate dispersal. Our results suggest that cells in aggregates actively determine whether to disperse or persist and undergo a transition to sporulation based on a self-produced cue related to the aggregate size. Identifying these cues is an important step in understanding and potentially manipulating bacterial cell-fate decisions.
