DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis

Eren, Furkan; Aslan, Mete; Kanarya, Dilek; Uysallı, Yiğit; Aydin, Musa; Kiraz, Berna; Aydın, Ömer; Kiraz, Alper

doi:10.1109/jbhi.2022.3203893

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…This consideration, along with the analysis of protoplast yield and viability, can help obtain predicted results that align more closely with experimental expectations. With the further application of deep learning in cell research, studies have been conducted to identify dead/surviving cells directly using convolutional neural networks [58], which makes it possible to count the number of living cells more easily. Additionally, the fitting ability of RSM and ANN models varies depending on the predicted objects [33,52,[59][60][61].…”

Section: Discussionmentioning

confidence: 99%

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

Guo,

Xi,

Guzailinuer

et al. 2024

Plant Methods

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Background Salsola laricifolia is a typical C3–C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies. Results Among the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L−1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts. Conclusion The current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

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Section: Discussionmentioning

confidence: 99%

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

Guo,

Xi,

Guzailinuer

et al. 2024

Plant Methods

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“…Besides spectroscopic data, microscope images can also be used as input data, which can be analyzed via deep learning-based image processing algorithms including convolutional neural networks [ 127 , 128 , 129 ].…”

Section: Soft Sensorsmentioning

confidence: 99%

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

et al. 2022

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In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

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“…during Design of Experiments) (Fig 1). Such advantages have been deployed in the counting of red blood cells and several other cell lines; however, to date an appropriate model for the counting of insect cells remains lacking [19,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Automated cell counting for Trypan blue-stained cell cultures using machine learning

Kuijpers,

van Veen,

van der Pol

et al. 2023

PLoS ONE

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Cell counting is a vital practice in the maintenance and manipulation of cell cultures. It is a crucial aspect of assessing cell viability and determining proliferation rates, which are integral to maintaining the health and functionality of a culture. Additionally, it is critical for establishing the time of infection in bioreactors and monitoring cell culture response to targeted infection over time. However, when cell counting is performed manually, the time involved can become substantial, particularly when multiple cultures need to be handled in parallel. Automated cell counters, which enable significant time reduction, are commercially available but remain relatively expensive. Here, we present a machine learning (ML) model based on YOLOv4 that is able to perform cell counts with a high accuracy (>95%) for Trypan blue-stained insect cells. Images of two distinctly different cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and Spodoptera frugiperda (Sf9), were used for training, validation, and testing of the model. The ML model yielded F1 scores of 0.97 and 0.96 for alive and dead cells, respectively, which represents a substantially improved performance over that of other cell counters. Furthermore, the ML model is versatile, as an F1 score of 0.96 was also obtained on images of Trypan blue-stained human embryonic kidney (HEK) cells that the model had not been trained on. Our implementation of the ML model comes with a straightforward user interface and can image in batches, which makes it highly suitable for the evaluation of multiple parallel cultures (e.g. in Design of Experiments). Overall, this approach for accurate classification of cells provides a fast, bias-free alternative to manual counting.

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DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis

Cited by 13 publications

References 35 publications

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

Automated cell counting for Trypan blue-stained cell cultures using machine learning

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