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.

中文翻译：

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人类外胚层模型的机器学习辅助成像分析

 抽象的

人类胚胎是一个复杂的结构，它的出现和发展是由内在遗传程序和细胞间相互作用指导的细胞级决策的结果。鉴于人类胚胎组织样本的可及性有限和相关的伦理限制，研究人员转向使用人类干细胞来生成胚胎模型来研究特定的胚胎发育步骤。然而，为了使用胚胎模型研究复杂的自组织发育事件，需要计算和成像工具来详细表征单细胞水平的细胞水平动力学。在这项工作中，我们从基于人多能干细胞（hPSC）的外胚层模型中获得了活细胞成像数据，该模型可以重现人胚泡植入后不久的腔内外胚层囊肿的形成。通过使用 CNN-LSTM 机器学习模型结合细胞跟踪和事件识别的 Python 管道处理成像数据，我们获得了腔内 hPSC 囊肿动态生长和形态发生过程中细胞状态和邻域变化的详细时间信息。该工具与感兴趣细胞类型的报告系相结合，将推动胚胎模型中 hPSC 命运规范的未来机制研究，并将增进我们对细胞水平决策如何导致全球组织和新兴现象的理解。洞察力、创新、整合：人类多能干细胞 (hPSC) 已成功用于模拟和理解人类胚胎发生过程中发生的细胞事件。 了解细胞-细胞和细胞-环境相互作用如何指导基于 hPSC 的胚胎模型中的细胞行为，是阐明驱动系统级胚胎模式和生长的机制的关键一步。在这项工作中，我们提出了一个强大的视频分析流程，其中结合了机器学习方法的使用，以充分表征 hPSC 自组织成腔囊肿的过程，以模拟人类囊胚植入后不久的腔内外胚层囊肿形成。该管道将​​成为了解胚胎模型中关键胚胎发生事件背后的细胞机制的有用工具。