Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

Joy, David A; Libby, Ashley R.G.; McDevitt, Todd C.

doi:10.1016/j.stemcr.2021.04.008

Cited by 19 publications

(15 citation statements)

References 52 publications

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“…Upon a demographical analysis of the collected data, the United States was the highest contributing country in this field with 9 studies out of 27 (33.3%) [ 25 , 26 , 29 , 30 , 38 , 45 , 46 , 47 , 48 ], followed by China (13.5%) [ 27 , 32 , 41 , 42 ], Japan (11.1%) [ 28 , 33 , 37 ], and Brazil [ 50 , 51 ] and Taiwan [ 32 , 40 ] (both 7.4%). Argentina [ 34 ], Finland [ 43 ], Germany [ 39 ], Italy [ 49 ], South Korea [ 36 ], and Thailand [ 44 ] each had one study each (3.7%), as shown in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…The number of publications studying stem cell image analysis using artificial intelligence increased throughout the years. Based on the included publications, the majority of published studies were in 2021 (seven studies) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…Imamura et al [ 28 ] built an ALS prediction model using a CNN-based deep learning algorithm where 4500, 1350, and 900 images were used for training, validation, and testing, respectively. Joy et al [ 29 ] trained five different neural networks (FCRN-A, FCRN-B, U-Net, Residual U-Net, and Count-ception) to localize each cell nucleus individually in a human-induced pluripotent stem cell (hiPSC) colony, generating longitudinal measures of cell and cellular neighborhood properties.…”

Section: Discussionmentioning

confidence: 99%

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Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Issa

Chaar

Kempisty

et al. 2022

Biology

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This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Issa

Chaar

Kempisty

et al. 2022

Biology

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“…Deep learning-based strategies have been developed to detect or predict cell differentiation. They either rely on morphological transformations of cell populations 22,30,31 , or on the detection of gained or lost expression of biomarkers 32,33 . In this study we propose an original approach, taking advantage of hydrogel microwells to isolate cells in suspension and to recapitulate the initial steps of the tumorsphere formation assay.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based detection of label-free cancer stem-like cell fate

Chambost

Berabez

Cochet‐Escartin

et al. 2022

Sci Rep

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The detection of cancer stem-like cells (CSCs) is mainly based on molecular markers or functional tests giving a posteriori results. Therefore label-free and real-time detection of single CSCs remains a difficult challenge. The recent development of microfluidics has made it possible to perform high-throughput single cell imaging under controlled conditions and geometries. Such a throughput requires adapted image analysis pipelines while providing the necessary amount of data for the development of machine-learning algorithms. In this paper, we provide a data-driven study to assess the complexity of brightfield time-lapses to monitor the fate of isolated cancer stem-like cells in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. We show that with our experimental system and on two different primary cell lines our optimized deep learning based algorithm outperforms classical computer vision and shallow learning-based algorithms in terms of accuracy while being faster than cutting-edge convolutional neural network (CNNs). With this study, we show that tailoring our deep learning-based algorithm to the image analysis problem yields better results than pre-trained models. As a result, such a rapid and accurate CNN is compatible with the rise of high-throughput data generation and opens the door to on-the-fly CSC fate analysis.

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“…Multiphoton imaging would also provide the possibility to do label free imaging of the seeded cells via metabolic markers [ 36 ]. For larger image data sets collected on 3D micro-ruler scaffolds interacting with cells deep learning approaches [ 37 , 38 ] may help extract further relevant information to help identify optimal cellular growth conditions for 3D cellular in vitro models.…”

Section: Discussionmentioning

confidence: 99%

3D Polymer Architectures for the Identification of Optimal Dimensions for Cellular Growth of 3D Cellular Models

et al. 2022

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Organ-on-chips and scaffolds for tissue engineering are vital assay tools for pre-clinical testing and prediction of human response to drugs and toxins, while providing an ethical sound replacement for animal testing. A success criterion for these models is the ability to have structural parameters for optimized performance. Here we show that two-photon polymerization fabrication can create 3D test platforms, where scaffold parameters can be directly analyzed by their effects on cell growth and movement. We design and fabricate a 3D grid structure, consisting of wall structures with niches of various dimensions for probing cell attachment and movement, while providing easy access for fluorescence imaging. The 3D structures are fabricated from bio-compatible polymer SZ2080 and subsequently seeded with A549 lung epithelia cells. The seeded structures are imaged with confocal microscopy, where spectral imaging with linear unmixing is used to separate auto-fluorescence scaffold contribution from the cell fluorescence. The volume of cellular material present in different sections of the structures is analyzed, to study the influence of structural parameters on cell distribution. Furthermore, time-lapse studies are performed to map the relation between scaffold parameters and cell movement. In the future, this kind of differentiated 3D growth platform, could be applied for optimized culture growth, cell differentiation, and advanced cell therapies.

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Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

Cited by 19 publications

References 52 publications

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Machine learning-based detection of label-free cancer stem-like cell fate

3D Polymer Architectures for the Identification of Optimal Dimensions for Cellular Growth of 3D Cellular Models

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