High or low RAX expression in differentiating aggregates foreshadows subsequent pituitary differentiation
Pituitary development requires interaction with the hypothalamus in vivo10,11. We have reproduced this developmental process in vitro by producing a hypothalamic-pituitary complex using pluripotent stem cells and have successfully induced a functional pituitary (Fig. 1a). To investigate the differentiation of precursor-cell aggregates into the hypothalamic-pituitary complex, we focused on RAX. This transcription factor is expressed first in the developing anterior neural region and later in the retina, hypothalamus, pineal gland, and other tissues or organs23. Given the reciprocal interaction between the hypothalamus and pituitary in development, the induction of both structures is likely to be satisfactory in aggregates that express RAX well. Conversely, in those that do not express RAX well, the pituitary would likely develop poorly (Fig. 1b).
a Schema of hypothalamic-pituitary interactions in vivo and in vitro. CK, cytokeratin. b Well-formed aggregates and poorly-formed aggregates. The RAX::VENUS cell line was used to confirm the quality of the aggregates. Scale bar = 500 μm. c Immunostaining of hESC aggregates at day 100 for ACTH (white) and LHX3 (red), markers of pituitary differentiation. For RAX::VENUS, spontaneous luminescence of the VENUS protein was imaged without staining. Scale bar = 100 μm. d Culture protocol for hypothalamic-pituitary organoid induction. Aggregates not expressing RAX at day 30 will not secrete ACTH after prolonged cultivation. e Aggregates with large and small RAX expression area groups show differences in ACTH secretion at day 100. Values shown on the graphs represent the means ± SEM, n = 9 independent experiments. *p < 0.05.
In our protocol, aggregates are transferred from 96-well plates to 10 cm dishes at day 30 of differentiation. Aggregates further cultured express ACTH, as demonstrated by immunostaining at day 100 (Fig. 1c, d). When aggregates with high and low RAX expression were cultured separately at 10 aggregates/10 ml from day 30, ACTH secretory capacity and the number of ACTH-positive cells at day 100 differed significantly (p = 0.03 and p = 0.004) between them (Fig. 1e, Supplementary Fig. 1). ACTH secretion from aggregates in the high RAX expression group was sufficient for transplantation experiments, as shown in previous studies8,24. We thus considered RAX expression at day 30 to be a marker for subsequent pituitary differentiation to determine whether differentiation was progressing as desired.
Datasets and models
We differentiated RAX::VENUS knock-in human ESCs (VA22-N37 /RIKEN RBC) to confirm RAX expression. In this study, we performed multiclass classification according to the area expressing RAX::VENUS at day 30 of differentiation. While it is difficult for experts to predict the gene expression of organoids in detail, multiclass classification allowed comparison between experts and models. Since a certain level of RAX expression is considered necessary for future ACTH secretory capacity, we defined category C as those with a RAX::VENUS positive area of less than 40%. Category C included all aggregates in the low RAX expression group. The remaining groups were divided into categories A and B, because more detailed prediction of high and low RAX could facilitate regression analysis in the future. We created the categories A (70 < %RAX), B (40 ≤ %RAX < 70), and C (%RAX < 40) and collected 500 bright-field images of aggregates in each category (Fig. 2a). The mean percentage of area marking for RAX::VENUS was 79.5% for A, 56.3% for B, and 21.6% for C (Fig. 2b). In each category we randomly designated respectively 400 and 100 images as training and test data. We performed multiclass classification of A, B, and C using EfficientNetV2-S25 and Vision Transformer22, released by Google LLC (Mountain View, CA) in 2021 and 2020 respectively. EfficientNetV2-S is an architecture with CNN structure, balancing depth, width, and resolution, leading to better performance with fewer parameters. Vision Transformer is a model that uses Transformer, used in the field of natural language processing, for image classification; with the Attention mechanism, the dependency of components can be captured better. As optimization methods, we used AdamW26 for EfficientNetV2-S and Adam27 for Vision Transformer, optimizers often used in each architecture. After respectively 100 and 20 epochs of training in each fold, we found no further improvement in accuracy and cross-entropy loss. For each architecture, we created 5 trained models using cross-validation. We saved the weights of the epochs with the lowest cross-entropy loss for each validation and used the average of the 5 trained model outputs for prediction (Fig. 2c).
a Categories grouped by RAX area percentage. b Distribution of images according to RAX area percentage. c Schema of the model.
Model performance
The accuracies of multiclass classification among A, B, and C were 67.3% for EfficientNetV2-S, 65.7% for Vision Transformer, and 70.0% for an ensemble model that used the average of the model outputs obtained from EfficientNetV2-S and Vision Transformer (Fig3a). In addition, we focused on category C to permit the removal of aggregates that are not successfully in the process of differentiation into the hypothalamic-pituitary complex at day 30. For category C, EfficiencyNetV2-S had a high sensitivity of 83.0% (95% confidence interval 74.2%–89.8%), specificity of 89.0% (95% confidence interval 83.8%–93.0%), and an F-value of 81.0%, while these values for Vision Transformer were respectively 77.0% (95% confidence interval 67.5%–84.8%), 93.0% (95% confidence interval 88.5%-96.1%), and 80.6%. The ensemble model showed a sensitivity of 82.0% (95% confidence interval 73.1%–89.0%), a specificity of 89.5% (95% confidence interval 84.4%–93.4%), and an F-value of 80.8%. We further evaluated the performance of the models in discriminating each category from the others by constructing receiver operating characteristic (ROC) curves and found that the area under the ROC curve was 86.5% for A, 72.4% for B, and 93.6% for C in EfficientNetV2-S, 87.5% for A, 73.6% for B, and 93.1% for C in Vision Transformer, and 87.8% for A, 74.5% for B, and 94.1% for C in the ensemble of the two models (Fig. 3b).
a Confusion matrix of EfficientNetV2-S, Vision Transformer, and the ensemble model. Cells filled with darker colors have many images distributed in them. b Receiver operating characteristic (ROC) curve for each category.
Comparison with expert performance
Using the test dataset of 300 bright-field images, we compared the classification performance of this deep-learning model with that of human experts. Three experts involved in cell culture in our laboratory for more than a year were instructed to predict the percentage of cultured-aggregate area expressing RAX using only the bright-field images. The accuracy of the experts varied from 46.7% to 60.0%, independent of years of experience (Fig. 4a). In discriminating C, in which RAX was poorly expressed, from A and B, expert-analysis sensitivity ranged from 56.0% to 73.0% and specificity from 84.0% to 86.0%. When sensitivity and specificity of expert predictions were plotted on the ROC curves of EfficientNetV2-S, Vision Transformer, and the ensemble of both models, all data for experts lay under the curve (Fig. 4b), indicating that the deep-learning models outperformed all experts in respect of both sensitivity and specificity (Fig. 4c).
a Confusion matrix of human experts. Cells filled with darker colors have many images distributed in them. b Receiver operating characteristic (ROC) curve for category C with human expert performance. All the models outperformed the experts. c Sensitivity and specificity of the models and of human experts. There were no significant differences among the models. All models were superior to experts in sensitivity. There were no significant differences between the human experts and the models in specificity. Values shown on the graphs represent the means ± 95%Cis, *p < 0.05, **p < 0.01, ***p < 0.001. Eff, EfficientNetV2-S; Vit, Vision Transformer; Ens, Ensemble Model; Exp1, Expert 1; Exp2, Expert 2; Exp3, Expert 3.
Model Visualization
To identify the regions that contributed most to neural network decisions, we used the Grad-CAM28 method for EfficientNetV2-S and the Deep ViT Features29 method for Vision Transformer to provide a visual description of the 300-test data. We identified the regions of the aggregates on which EfficientNetV2-S focused attention by outputting a heat map with Grad-CAM. In EfficientNetV2-S, 96.3% of the images of aggregates with predicted labels of A involved the periphery of the aggregates. In addition, EfficientNetV2-S often focused on cystic regions in images of poorly formed aggregates, and 76.8% of all images with cysts were focused on the cystic area (Fig. 5a). For Vision Transformer, we used Deep ViT Features to perform principal component analysis (PCA) and to visualize the informative components. The dense key features of the last transformation block of the ViT were processed with PCA. Principal component (PC) 1 reflected the structures of the 96-well plate used in this study. PC2 reflected the parenchymal parts of the aggregates. PC3 reflected the periphery of the aggregates and some cysts. PC4 reflected the center of the aggregates. (Fig. 5b).
a Representative examples of the heatmaps of EfficientNetV2-S generated by Grad-CAM and a tabulation of where attention was focused. EfficientNetV2-S focuses on specific areas such as the periphery and cysts, just as did the experts. b Representative examples of the results of principal component analysis of Vision Transformer using Deep ViT Features. Merge images were generated with PC2, PC3, and PC4, which are thought to be involved in the classification of aggregates. PC1 principal component 1, PC2 principal component 2, PC3 principal component 3, PC4 principal component 4.
Prediction of differentiation of organoids without RAX::VENUS
Using RAX::VENUS knock-in cells (and thus observing RAX expression during differentiation) permits aggregate quality assessment. However, modifying RAX::VENUS impedes its clinical application, such as in transplantation. To address this, we investigated whether our model could be applied to KhES-1 cells (RIKEN RBC), without the use of RAX::VENUS, to assess aggregate quality in a similar manner.
We classified KhES-1 cells at day 30 of differentiation using the ensemble model of EfficientNetV2-S and Vision Transformer trained on VA22-N37 cells (with RAX::VENUS). To boost accuracy, 1350 of the 1500 images obtained from VA22N37 were used for training, and no images from KhES-1 were used. The model’s accuracy was 72.0% (Fig. 6a). When the model was applied to aggregate images obtained from KhES-1, 937 aggregate images were classified into 633 category A, 209 category B, and 95 category C (Fig. 6b). Among the aggregates obtained from KhES-1 cells, on immunostaining those that the model classified as A showed more RAX expression and those classified as C showed less RAX expression (Fig. 6c). Furthermore, when aggregates in each category were divided into 10 aggregates/10 ml individually and cultured, ACTH secretory capacity from highest to lowest matched categories A, B, and C in that order. At day 100 those classified as A had significantly higher ACTH secretory capacity than those classified as C (n = 9, p = 0.004) (Fig. 6d). The aggregates classified as A by the model were not cystic even at day 100.
a Confusion matrix of the ensemble model trained using 1350 images. b Schematic of model diversion. Prediction of categories by applying the trained model to aggregates derived from KhES-1. c Immunostaining of aggregates derived from KhES-1 on day30. Left: Predicted by the model as A. Right: Predicted to be C. Scale bar, 500 μm. d Comparison of ACTH secretory capacity at day 100 among three categories. Those predicted to be A had significantly higher ACTH secretory capacity. Values shown on the graphs represent the means ± SEM, n = 9 independent experiments. **p < 0.01.
Analysis of classified aggregates
We confirmed the differences in the molecular basis of the KhES-1 aggregates, as classified by the model, using relevant markers. Specifically, we confirmed the gene expression of the day 30 aggregates, which were classified as category A and category C, through immunostaining. This involved staining central nervous system markers (CDH2 and SOX1), hypothalamic markers (RAX, NKX2.1, and PAX6), and an oral ectoderm marker (PITX1), all of which are expected to be expressed in hypothalamic-pituitary organoids at day 30. Our findings revealed that RAX, a marker used for prediction, and central nervous system markers CDH2 and SOX1 were significantly more expressed in aggregates predicted to be category A, while other hypothalamic (NKX2.1 and PAX6) and pituitary markers showed no significant difference in their expression levels (Fig. 7).
Rate of immunostaining positive cells in each aggregate. Values shown on the graphs represent the means ± SEM, n = 3 independent experiments. *p < 0.05, ∗∗p < 0.01.
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- Source: https://www.nature.com/articles/s42003-024-07109-1