A patterned human neural tube model using microfluidic gradients.

The human nervous system is a highly complex but organized organ. The foundation of its complexity and organization is laid down during regional patterning of the neural tube, the embryonic precursor to the human nervous system. Historically, studies of neural tube patterning have relied on animal models to uncover underlying principles. Recently, models of neurodevelopment based on human pluripotent stem cells, including neural organoids1-5 and bioengineered neural tube development models6-10, have emerged. However, such models fail to recapitulate neural patterning along both rostral-caudal and dorsal-ventral axes in a three-dimensional tubular geometry, a hallmark of neural tube development. Here we report a human pluripotent stem cell-based, microfluidic neural tube-like structure, the development of which recapitulates several crucial aspects of neural patterning in brain and spinal cord regions and along rostral-caudal and dorsal-ventral axes. This structure was utilized for studying neuronal lineage development, which revealed pre-patterning of axial identities of neural crest progenitors and functional roles of neuromesodermal progenitors and the caudal gene CDX2 in spinal cord and trunk neural crest development. We further developed dorsal-ventral patterned microfluidic forebrain-like structures with spatially segregated dorsal and ventral regions and layered apicobasal cellular organizations that mimic development of the human forebrain pallium and subpallium, respectively. Together, these microfluidics-based neurodevelopment models provide three-dimensional lumenal tissue architectures with in vivo-like spatiotemporal cell differentiation and organization, which will facilitate the study of human neurodevelopment and disease.

Single-cell analysis of embryoids reveals lineage diversification roadmaps of early human development.

Despite its clinical and fundamental importance, our understanding of early human development remains limited. Stem cell-derived, embryo-like structures (or embryoids) allowing studies of early development without using natural embryos can potentially help fill the knowledge gap of human development. Herein, transcriptome at the single-cell level of a human embryoid model was profiled at different time points. Molecular maps of lineage diversifications from the pluripotent human epiblast toward the amniotic ectoderm, primitive streak/mesoderm, and primordial germ cells were constructed and compared with in vivo primate data. The comparative transcriptome analyses reveal a critical role of NODAL signaling in human mesoderm and primordial germ cell specification, which is further functionally validated. Through comparative transcriptome analyses and validations with human blastocysts and in vitro cultured cynomolgus embryos, we further proposed stringent criteria for distinguishing between human blastocyst trophectoderm and early amniotic ectoderm cells.

Machine learning-assisted imaging analysis of a human epiblast model.

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.