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Updated: Nov 28, 2025
In Vitro Differentiation of Human Pluripotent Stem Cells into Trophoblastic Cells
Published on: March 16, 2017
Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural
Yajun Liu1, Yi Zhang2, Jinquan Cui1
1Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Henan Key Laboratory for Gynecological Oncology Medicine, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Academy of Medical Sciences of Zhengzhou University Translational Medicine Platform, Zhengzhou University, China.
Deep learning models can automatically identify human stem cells and trophoblast cells. However, distinguishing between trophoblast-like cells and immortalized cell lines remains a challenge.
Area of Science:
- Developmental biology
- Cell biology
- Biotechnology
Background:
- Understanding early human pregnancy requires studying stem cell differentiation into trophoblast cells.
- Automated identification of these processes using deep learning can accelerate research.
Purpose of the Study:
- To apply deep learning for automated classification of embryonic stem cells (ESCs) and differentiated trophoblast-like cells (TBL).
- To evaluate the performance of various convolutional neural networks (CNNs) for this classification task.
Main Methods:
- Utilized transfer learning with CNNs to distinguish between ESCs and TBLs from microscopic images.
- Employed data augmentation strategies to address limited training data.
- Compared the efficacy of DenseNet, VGG16, VGG19, InceptionV3, and Xception models.
Main Results:
- The CNN models successfully differentiated between ESCs and trophoblast cells.
- The models could not distinguish between TBLs and immortalized trophoblast cell lines (JEG-3, HTR8-SVneo).
Conclusions:
- Deep learning shows promise for automated trophoblast cell phenotype classification.
- Further refinement is needed for distinguishing specific trophoblast cell subtypes.
