Quality evaluation of induced pluripotent stem cell colonies by fusing multi-source features

Application Prospect of Induced Pluripotent Stem Cells in Organoids and Cell Therapy

Since its inception, induced pluripotent stem cell (iPSC) technology has been hailed as a powerful tool for comprehending disease etiology and advancing drug screening across various domains. While earlier iPSC-based disease modeling and drug assessment primarily operated at the cellular level, recent years have witnessed a significant shift towards organoid-based investigations. Organoids derived from iPSCs offer distinct advantages, particularly in enabling the observation of disease progression and drug metabolism in an in vivo-like environment, surpassing the capabilities of iPSC-derived cells. Furthermore, iPSC-based cell therapy has emerged as a focal point of clinical interest. In this review, we provide an extensive overview of non-integrative reprogramming methods that have evolved since the inception of iPSC technology. We also deliver a comprehensive examination of iPSC-derived organoids, spanning the realms of the nervous system, cardiovascular system, and oncology, as well as systematically elucidate recent advancements in iPSC-related cell therapies.

Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods

BACKGROUND AND OBJECTIVE

Human induced pluripotent stem cells (hiPSCs) represent an ideal source for patient specific cell-based regenerative medicine; however, efficiency of hiPSC formation from reprogramming cells is low. We use several deep-learning results from time-lapse brightfield microscopy images during culture, to early detect the cells potentially reprogramming into hiPSCs and predict the colony morphology of these cells for improving efficiency of culturing a new hiPSC line.

METHODS

Sets of time-lapse bright-field images are taken to track reprogramming process of CD34+ cells biologically identified as just beginning reprogramming. Prior the experiment, 9 classes of templates with distinct cell features clipped from microscopy images at various reprogramming stages are used to train a CNN model. The CNN is then used to classify a microscopy image as probability images of these classes. Probability images of some class are used to train a densely connected convolutional network for extracting regions of this class on a microscopy image. A U-net is trained to segment cells on the time-lapse images in early reprogramming stage during culture. The segmented cells are classified by the extracted regions to count various types of cells appearing in the early reprogramming stage for predicting the identified cells potentially forming hiPSCs. The probability images of hiPSC classes are also used to train a spatiotemporal RNN for predicting the future hiPSC colony morphology of the potential cells.

RESULTS

Experimental results show the prediction (before 7 days after of beginning of the reprogramming) achieved 0.8 accuracy, and 66% of the identified cells under different culture conditions, predicted as forming, finally formed hiPSCs. The predicted hiPSC images and extracted colonies on the images show the prediction for future 1.5 days achieved high accuracy of hiPSC colony areas and image similarity.

CONCLUSIONS

Our study proposes a method using several deep learning models to efficiently select the reprogramming cells possibly forming hiPSCs and to predict the shapes of growing hiPSC colonies. The proposed method is expected to improve the efficiency when establishing a new hiPSC line culture.

Development of the T-ALLiPSC-based therapeutic cancer vaccines for T-cell acute lymphoblastic leukemia

The T-cell acute lymphoblastic leukemia (T-ALL) is a kind of hematological malignancy in children. Despite the significant improvement in the cure rate of T-ALL upon treatment with chemotherapy regimens, steroids, and allotransplantation there are relapses. This study focuses on the tumor-specific therapeutic vaccines derived from the induced pluripotent stem cells (iPSC) to address the issue of T-ALL recurrence. Patient-derived tumor cells and healthy donor cells were reprogrammed into the iPSCs and the RNA-seq data of the T-ALL-iPSCs and H-iPSCs were analyzed. In vitro, the whole-cell lysate antigens of iPSCs were prepared to induce the dendritic cells (DC) maturation, which in turn stimulated the tumor-specific T cells to kill the T-ALL tumor cells (Jurkat, CCRF-CEM, MOLT-4). The cytotoxic T lymphocyte stimulated by the DC-loaded T-ALL-iPSC-derived antigens showed specific cytotoxicity against the T-ALL cells in vitro. In conclusion, the T-ALL-iPSC-based therapeutic cancer vaccine can elicit a specific anti-tumor effect on T-ALL.