Deep Learning Powered Identification of Differentiated Early Mesoderm Cells from Pluripotent Stem Cells.

Pluripotent stem cells can be differentiated into all three germ-layers including ecto-, endo-, and mesoderm in vitro. However, the early identification and rapid characterization of each germ-layer in response to chemical and physical induction of differentiation is limited. This is a long-standing issue for rapid and high-throughput screening to determine lineage specification efficiency. Here, we present deep learning (DL) methodologies for predicting and classifying early mesoderm cells differentiated from embryoid bodies (EBs) based on cellular and nuclear morphologies. Using a transgenic murine embryonic stem cell (mESC) line, namely OGTR1, we validated the upregulation of mesodermal genes (Brachyury (T): DsRed) in cells derived from EBs for the deep learning model training. Cells were classified into mesodermal and non-mesodermal (representing endo- and ectoderm) classes using a convolutional neural network (CNN) model called InceptionV3 which achieved a very high classification accuracy of 97% for phase images and 90% for nuclei images. In addition, we also performed image segmentation using an Attention U-Net CNN and obtained a mean intersection over union of 61% and 69% for phase-contrast and nuclear images, respectively. This work highlights the potential of integrating cell culture, imaging technologies, and deep learning methodologies in identifying lineage specification, thus contributing to the advancements in regenerative medicine. Collectively, our trained deep learning models can predict the mesoderm cells with high accuracy based on cellular and nuclear morphologies.

Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population.

Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification of pancreatic TRCs remain technically challenging. Here, we developed deep learning (DL) models paired with machine learning (ML) models to readily identify and classify 3D fibrin gel-selected TRCs into sub-types. Using four different human pancreatic cell lines, namely, MIA PaCa-2, PANC-1, CFPAC-1, and HPAF-II, we classified 3 main sub-types to be present within the TRC population. Our best model was an Inception-v3 convolutional neural network (CNN) used as a feature extractor paired with a Support Vector Machine (SVM) classifier with radial basis function (rbf) kernel which obtained a test accuracy of 90%. In addition, we compared this hybrid method of supervised classification with other methods of supervised classifications and showed that our working model outperforms others. With the help of unsupervised machine learning algorithms, we also validated that the pancreatic TRC subpopulation can be clustered into 3 sub-types. Collectively, our robust model can detect and readily classify tumorigenic TRC subpopulation label-free in a high-throughput fashion which can be very beneficial in clinical settings.