We thank Qiushi Sun, Yao Wang, Yu Xia, Jinyu Yang, Chang Lin, Zimu Cen, Dongdong Liang, Rong Wei, Ze Xu, Guangyin Xi, Gang Xue, Can Ye, Li-Peng Wang, Peng Zou, Shi-Qiang Wang, Pablo Rivera-Fuentes, Salome P&#252;ntener, Zhixing Chen, Yi Liu, and Jue Zhang, for laying the groundwork of this strategy. This work was supported by the National Key R&#38;D Program of China (2018YFA0800504, 2019YFA0110000) and the Space Medical Experiment Project of China Manned Space Program (HYZHXM01020) to Yang Zhao. Figure 1 was created with BioRender.com.

Monitoring Pluripotent Stem Cell Differentiation by Image-Based ML Strategy

Yang Zhao, Jue Zhang, Xiaochun Yang, Yao Wang, and Daichao Chen are filing a patent for the PSC differentiation strategy reported in this paper (202210525166.X).

Here, we described a detailed protocol to overcome one of the major problems in current PSC application and translation&#8212;the variability in cell differentiation. By harnessing live-cell brightfield imaging and ML, we iteratively optimized PSC differentiation to achieve consistently high efficiency across cell lines and batches. However, in the above differentiation process, several critical steps in the protocol have a decisive influence on whether the differentiation would succeed or not. Since the cell state in th...

Pluripotent stem cells (PSCs) possess the remarkable ability to differentiate into many types of cells in vitro. These differentiated functional cells could be used for cell therapy, disease modeling, and drug development, all valuable for research or clinical applications1,2,3. For example, a variety of methods have been developed to differentiate PSCs into cardiomyocytes (CMs)4,5,6,7. These CMs can be applied for cardiotoxicity testing of drugs, modeling of heart disease, and cell transplantation8,9,10,11.
However, the conversion from PSC to the terminal differentiated cells is a stepwise process, and multiple perturbations during the differentiation process may lead cells to divergent cell fates. Different genetic backgrounds and epigenetic marks of PSC lines influence the potential for differentiation to a specific lineage12,13,14,15; the number of PSC passages and accumulated gene mutations are also sources of PSC heterogeneity; differences in the experimental operations employed by different experimenters can also lead to completely different differentiation results16,17,18,19,20. Therefore, currently one of the main problems in PSC-derived cell production is the instability among cell lines and batches21,22,23,24,25. Instability in PSC differentiation often leads to multiple repeated experiments, consuming significant time and labor resources. To address this issue, it is crucial to develop a strategy that minimizes the variability among cell lines and batches, thus enhancing the stability and robustness of the differentiation.
Recently, advances in high-resolution microscopy and machine learning (ML) have facilitated the application of ML-based quantitative image analysis in cell biology, making it possible to utilize valuable information in cell imaging features26,27,28,29,30,31,32,33,34. In our previous work, we proposed a live-cell image-based ML strategy to monitor and intervene in the PSC differentiation status in real time to improve the stability and efficiency of the PSC differentiation (Figure 1)35. Taking PSC-to-cardiomyocyte differentiation as an example, we evaluated the initial PSC state using random forest models, predicted the optimal differentiation condition using logistic regression models, and recognized successfully differentiated cells using deep learning-based Grad-CAM36 and pix2pix37. ML models learned to identify cell lineages from a range of bright-field morphological features, including features about area, circumference, convexity, solidity, brightness, moving velocity, and other implicit features extracted by deep convolutional neural networks. Based on inference from these established ML models, we realized control of the initial PSC state, early assessment and intervention in differentiation conditions, and elimination of the misdifferentiated cell contamination, together providing a comprehensive and accurate modulation of the cardiac differentiation process. Here we provide a step-by-step protocol for developing the strategy.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.25% Trypsin-EDTA</td>
			<td>Gibco</td>
			<td>25200056</td>
			<td>Diluted digests were used for CPC and CM digestion</td>
		</tr>
		<tr>
			<td>4% Paraformaldehyde in PBS</td>
			<td>KeyGEN BioTECH</td>
			<td>KGIHC016</td>
			<td></td>
		</tr>
		<tr>
			<td>6-well Cell Culture Plate</td>
			<td>NEST</td>
			<td>703001</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well Cell Culture Plate</td>
			<td>NEST</td>
			<td>701001</td>
			<td></td>
		</tr>
		<tr>
			<td>B27 Supplement</td>
			<td>Gibco</td>
			<td>17504044</td>
			<td></td>
		</tr>
		<tr>
			<td>B27 Supplement Minus Insulin</td>
			<td>Gibco</td>
			<td>A1895601</td>
			<td></td>
		</tr>
		<tr>
			<td>Bovine serum albumin (BSA)</td>
			<td>GPC BIOTECH</td>
			<td>AA904-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Celldiscoverer 7</td>
			<td>Zeiss</td>
			<td></td>
			<td>Instruments used to take bright-field images throughout differentiation and final cTnT images</td>
		</tr>
		<tr>
			<td>CHIR99021</td>
			<td>Selleck</td>
			<td>S1263</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM/F12</td>
			<td>Gibco</td>
			<td>12634010</td>
			<td></td>
		</tr>
		<tr>
			<td>Donkey anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 488</td>
			<td>Thermo</td>
			<td>A-21202</td>
			<td>Secondary Antibody</td>
		</tr>
		<tr>
			<td>FACSAria III</td>
			<td>BD Biosciences</td>
			<td></td>
			<td>Flow cytometry sorter</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum (FBS)</td>
			<td>VISTECH</td>
			<td>SE100-B</td>
			<td></td>
		</tr>
		<tr>
			<td>Hoechst 33342</td>
			<td>YEASEN</td>
			<td>40732ES03</td>
			<td></td>
		</tr>
		<tr>
			<td>Human Pluripotent Stem Cell Chemical-defined Medium</td>
			<td>Cauliscell Inc</td>
			<td>400105</td>
			<td>Basal medium of PSC preparation medium</td>
		</tr>
		<tr>
			<td>iPS-18</td>
			<td>TaKaRa</td>
			<td>Y00300</td>
			<td></td>
		</tr>
		<tr>
			<td>iPS-B1</td>
			<td>Cellapy</td>
			<td>CA4025106</td>
			<td></td>
		</tr>
		<tr>
			<td>iPS-F</td>
			<td>Nuwacell</td>
			<td>RC01001-B</td>
			<td></td>
		</tr>
		<tr>
			<td>iPS-M</td>
			<td>Nuwacell</td>
			<td>RC01001-A</td>
			<td></td>
		</tr>
		<tr>
			<td>IWR1-1-endo</td>
			<td>Selleck</td>
			<td>S7086</td>
			<td>IWR1</td>
		</tr>
		<tr>
			<td>Jupyter Notebook</td>
			<td>N/A</td>
			<td>Version 6.4.0</td>
			<td><a href="https://jupyter.org/">https://jupyter.org/</a></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>Version R2020a</td>
			<td>Software for scientific computation and image annotation</td>
		</tr>
		<tr>
			<td>Matrigel Matrix</td>
			<td>Corning</td>
			<td>354230</td>
			<td>Matrigel</td>
		</tr>
		<tr>
			<td>Mouse monoclonal IgG1 anti-cTnT</td>
			<td>Thermo</td>
			<td>MA5-12960</td>
			<td>cTnT primary antibody</td>
		</tr>
		<tr>
			<td>Normal Donkey Serum</td>
			<td>Jackson</td>
			<td>017-000-121</td>
			<td></td>
		</tr>
		<tr>
			<td>ORCA-Flash 4.0 V3 digital CMOS camera</td>
			<td>Hamamatsu</td>
			<td>C13440-20CU</td>
			<td>The digital camera assembled on Celldiscoverer7</td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>NEB</td>
			<td>21-040-CVR</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>Gibco</td>
			<td>15140-122</td>
			<td></td>
		</tr>
		<tr>
			<td>Pluripotency Growth Mater 1 basal medium</td>
			<td>Cellapy</td>
			<td>CA1007500-1</td>
			<td>Basal medium of PSC culture medium</td>
		</tr>
		<tr>
			<td>Pluripotency Growth Mater 1 supplement</td>
			<td>Cellapy</td>
			<td>CA1007500-2</td>
			<td>Supplement of PSC culture medium</td>
		</tr>
		<tr>
			<td>Prism</td>
			<td>Graphpad</td>
			<td>Version 8/9</td>
			<td>Statistical software for statistical analysis and plotting</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>N/A</td>
			<td>version 3.6</td>
			<td>Python 3 environment for scientific computation, with packages pytorch (1.9.0), numpy, scipy, pandas, visdom, scikit-learn, scikit-image, opencv-python, and matplotlib software for scientific computation and image annotation.</td>
		</tr>
		<tr>
			<td>RPMI 1640</td>
			<td>Gibco</td>
			<td>11875176</td>
			<td></td>
		</tr>
		<tr>
			<td>Supplement hPSC-CDM (500x)</td>
			<td>Cauliscell Inc</td>
			<td>00015</td>
			<td>Supplement of PSC preparation medium</td>
		</tr>
		<tr>
			<td>TiE</td>
			<td>Nikon</td>
			<td></td>
			<td>An inverted fluorescence microscope (with modification) for region-selevtive purification</td>
		</tr>
		<tr>
			<td>Triton&#160;X-100</td>
			<td>Amresco</td>
			<td>9002-93-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Versene Solution</td>
			<td>Thermo</td>
			<td>15040066</td>
			<td>EDTA solution for PSC digestion</td>
		</tr>
		<tr>
			<td>Y27632</td>
			<td>Selleck</td>
			<td>S6390</td>
			<td></td>
		</tr>
		<tr>
			<td>Zen</td>
			<td>Zeiss</td>
			<td>Version 3.1</td>
			<td>A supporting software of Celldiscoverer7 for&#160; image acquisition, processing and analysis</td>
		</tr>
	</tbody>
</table>

a live-cell image-based machine learning strategy to monitor pluripotent stem cell differentiation

Pluripotent stem cell (PSC) technologies have been widely used in drug discovery, disease modeling, and regenerative medicine.&#160;However, available PSC-to-functional cell differentiation systems are impeded by problems of severe line-to-line and batch-to-batch variability. Precise control of cell differentiation in real time is therefore important. In this protocol, we describe a non-invasive and intelligent strategy that overcomes the variability in cell differentiation by using bright-field image-based machine learning. Taking PSC-to-cardiomyocyte differentiation as an example, this methodology provides detailed information for control of the initial PSC state, early assessment and intervention in differentiation conditions, and elimination of the misdifferentiated cell contamination, together realizing consistently high-quality differentiation from PSCs to functional cells. In principle, this strategy can be extended to other cell differentiation or reprogramming systems with multiple steps to support cell manufacturing, as well as to further our understanding of the mechanisms during cell fate conversion.

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

In this study, based on live-cell bright-field images, we developed a strategy, harnessing different machine learning models. This strategy can identify cell lineage non-invasively, modulate the differentiation process in real-time, and optimize the differentiation protocol, improving the invulnerability in PSC-to-functional cell differentiation. Pluripotent stem cells present the ability to differentiate into many types of cells in vitro, which could be used for cell therapy, disease modeling, and drug development.One of the main problems in PSC-derived cell production, is the instability among cell lines and batches. It often leads to multiple repeated experiments, consuming significant time and labor. Currently, state-of-the-art microscopic technologies could support long-term time-lapse, high-throughput image acquisition on live cells.Meanwhile, the fast evolving machine learning method is being increasingly applied in cellular image analysis, which is opening possibilities to recognize specific cellular constituents or cell images during differentiation in cell culture. This method could hopefully be applied to standardized other cell fate induction systems like organoid differentiation, direct transdifferentiation, or cellular programming, especially for unstable systems requiring multiple steps and complex inducers. Also, our strategy is compatible with other technologies, which promisingly can be integrated into a self-adaptive and closed-loop system for full-automatic processing of PSC differentiation in vitro.

&#160;Based on brightfield imaging and ML, the overall differentiation process can be intelligently monitored and optimized. At the PSC stage, we developed an ML model that could predict the final differentiation efficiency according to the morphological features of initial PSC colonies, to determine the most suitable or appropriate time point to initiate differentiation (Figure 4A,B). The differentiation efficiency predicted by the random forest model is highly correlated w...

Available pluripotent stem cell (PSC)-to-functional cell differentiation systems are currently impeded by problems of severe line-to-line and batch-to-batch variability. Here, using cardiac differentiation as the main example, we present a protocol to intelligently monitor and modulate the process of PSC differentiation based on image-based machine learning.

Pluripotent stem cell (PSC) technologies have been widely used in drug discovery, disease modeling, and regenerative medicine.&#160;However, available ...

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243 Views.  Peking University. In this study, based on live-cell bright-field images, we developed a strategy, harnessing different machine learning models. This strategy can identify cell lineage non-invasively, modulate the differentiation process in real-time, and optimize the differentiation protocol, improving the invulnerability in PSC-to-functional cell differentiation. Pluripotent stem cells present the ability to differentiate into many types of cells in vitro, which could be used for cell therapy, disease modelin...