Predicting multipotency of human adult stem cells derived from various donors through deep learning

Kim, Hyeonji; Park, Keonhyeok; Yon, Jung-Min; Kim, Sung Won; Lee, Soo Young; Jeong, Iljoo; Jang, Jinah; Lee, Seung−Chul; Cho, Dong‐Woo

doi:10.1038/s41598-022-25423-8

Cited by 8 publications

(5 citation statements)

References 37 publications

(34 reference statements)

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“…proposed a new prediction method with a CNN model (DenseNet121) to evaluate the multipotency rate of Human nasal turbinate stem cells (hNTSCs) using fluorescence images by characterizing genes and morphologies. The CNN model classified multipotent cells comparatively well with 85.98% accuracy, which well-matched results of actual differentiation ( 33 ).…”

Section: Deep Learning For Stem Cell Researchsupporting

confidence: 74%

“…To collect data for a model system, they created and used NSCs in different phases with differentiation inducers: neurons, astrocytes, and oligodendrocytes. Notably, the (33).…”

Section: Application Of Deep Learning On Cscsmentioning

confidence: 99%

“…Improvements in microscopy, computational capabilities, and data analysis have enabled high-throughput, high-content approaches from endpoint 2D microscopy images to 3D microscopy images. This approach has been engaged in various cell biology research activities ( 28 – 30 ), specifically for stem cell characterization ( 31 – 33 ) and differentiation pattern ( 34 – 37 ), disease modeling ( 38 , 39 ), and drug screening and discovery ( 40 , 41 ). The advantages of this technique are that it is non-invasive, high-throughput, and consistent, which can save time and costs once automated.…”

Section: Deep Learning For Stem Cell Researchmentioning

confidence: 99%

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

Chen

et al. 2023

Front. Immunol.

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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.

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Section: Deep Learning For Stem Cell Researchsupporting

confidence: 74%

“…To collect data for a model system, they created and used NSCs in different phases with differentiation inducers: neurons, astrocytes, and oligodendrocytes. Notably, the (33).…”

Section: Application Of Deep Learning On Cscsmentioning

confidence: 99%

Section: Deep Learning For Stem Cell Researchmentioning

confidence: 99%

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

Chen

et al. 2023

Front. Immunol.

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“…Therefore, the identification of IAVs host tropism has been an important research issue. Meanwhile, deep learning has been widely applied in the fields of protein structure prediction, protein function prediction, and genetic engineering, and is vastly promising for the host tropism investigation of IAVs [30][31][32]. Benefitted from this, we used a powerful deep network to distinguish viral tropism in different hosts more effectively and efficiently.…”

Section: Discussionmentioning

confidence: 99%

Flu-CNN: predicting host tropism of influenza A viruses via character-level convolutional networks

Luo,

Wang,

Wang

et al. 2023

Preprint

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Throughout history, Influenza A viruses (IAVs) have caused significant harm and catastrophic pandemics. The presence of host barriers results in viral host tropism, where infected hosts are subject to strict restrictions due to the hindered spread of viruses across hosts. Therefore, the identification of host tropism of IAVs, particularly in humans, is crucial to preventing the cross-host transmission of avian viruses and their outbreaks in humans. Nevertheless, efficiently and effectively identifying host tropism, especially for early host susceptibility warnings based on viral genome sequences during outbreak onset, remains challenging. To address this challenge, we propose Flu-CNN, a deep neural network model based on classical character-level convolutional networks. By analyzing the genomic segments of IAVs, Flu-CNN can accurately identify the host tropism, with a particular focus on avian influenza viruses that may infect humans. According to our experimental evaluations, Flu-CNN achieved an accuracy of 99% in identifying virus hosts via only a single genomic segment, even for subtypes with a relatively small number of viral strains such as H5N1, H7N9, and H9N2. The superiority of Flu-CNN demonstrates its effectiveness in screening for critical amino acid mutations, which is important to host adaptation, and zoonotic risk prediction of viral strains. Flu-CNN is a valuable tool for identifying evolutionary characterization, monitoring potential outbreaks, and preventing epidemical spreads of IAVs, which contribute to the effective surveillance of influenza A viruses.

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“…A transfer learning-based approach was utilized as the feature extractor predicting, with four well-performing models (VGG 19, InceptionV335, Xception, and DenseNet121) pre-trained on ImageNet. With over 85% accuracy, the results demonstrated the potential of a computer vision based method for identifying stem cell differentiation ( Kim et al, 2022b ). More recently, Zhou et al introduced a predictive model for classifying hMSC differentiation lineages using the k-nearest neighbors (kNN) algorithm ( Zhou et al, 2023 ).…”

Section: Introductionmentioning

confidence: 95%

Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs)

Mai,

Luo,

Fasciano

et al. 2023

Front. Cell Dev. Biol.

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Human mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes. These cells have been extensively employed in the field of cell-based therapies and regenerative medicine due to their inherent attributes of self-renewal and multipotency. Traditional approaches for assessing hMSCs differentiation capacity have relied heavily on labor-intensive techniques, such as RT-PCR, immunostaining, and Western blot, to identify specific biomarkers. However, these methods are not only time-consuming and economically demanding, but also require the fixation of cells, resulting in the loss of temporal data. Consequently, there is an emerging need for a more efficient and precise approach to predict hMSCs differentiation in live cells, particularly for osteogenic and adipogenic differentiation. In response to this need, we developed innovative approaches that combine live-cell imaging with cutting-edge deep learning techniques, specifically employing a convolutional neural network (CNN) to meticulously classify osteogenic and adipogenic differentiation. Specifically, four notable pre-trained CNN models, VGG 19, Inception V3, ResNet 18, and ResNet 50, were developed and tested for identifying adipogenic and osteogenic differentiated cells based on cell morphology changes. We rigorously evaluated the performance of these four models concerning binary and multi-class classification of differentiated cells at various time intervals, focusing on pivotal metrics such as accuracy, the area under the receiver operating characteristic curve (AUC), sensitivity, precision, and F1-score. Among these four different models, ResNet 50 has proven to be the most effective choice with the highest accuracy (0.9572 for binary, 0.9474 for multi-class) and AUC (0.9958 for binary, 0.9836 for multi-class) in both multi-class and binary classification tasks. Although VGG 19 matched the accuracy of ResNet 50 in both tasks, ResNet 50 consistently outperformed it in terms of AUC, underscoring its superior effectiveness in identifying differentiated cells. Overall, our study demonstrated the capability to use a CNN approach to predict stem cell fate based on morphology changes, which will potentially provide insights for the application of cell-based therapy and advance our understanding of regenerative medicine.

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Predicting multipotency of human adult stem cells derived from various donors through deep learning

Cited by 8 publications

References 37 publications

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

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

Flu-CNN: predicting host tropism of influenza A viruses via character-level convolutional networks

Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs)

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