Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network

Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to, that is, distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation toward hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.

Regulation of signaling pathways in hair follicle stem cells

Hair follicle stem cells (HFSCs) reside in the bulge region of the outer root sheath of the hair follicle. They are considered slow-cycling cells that are endowed with multilineage differentiation potential and superior proliferative capacity. The normal morphology and periodic growth of HFSCs play a significant role in normal skin functions, wound repair and skin regeneration. The HFSCs involved in these pathophysiological processes are regulated by a series of cell signal transduction pathways, such as lymphoid enhancer factor/T-cell factor, Wnt/β-catenin, transforming growth factor-β/bone morphogenetic protein, Notch and Hedgehog. The mechanisms of the interactions among these signaling pathways and their regulatory effects on HFSCs have been previously studied, but many mechanisms are still unclear. This article reviews the regulation of hair follicles, HFSCs and related signaling pathways, with the aims of summarizing previous research results, revealing the regulatory mechanisms of HFSC proliferation and differentiation and providing important references and new ideas for treating clinical diseases.