hiPSC disease modeling with 3D organoids

AI‐organoid integrated systems for biomedical studies and applications

“…Organoids are three‐dimensional (3D) cell constructs that can be directly derived from human pluripotent stem cells (hPSCs). An organoid contains a complex multicellular cluster of organ‐specific cells that closely resemble the structure and function of an organ 1,2 . Due to their striking similarity to organs, organoids have been proven to be beneficial for the study of organ development and human diseases 3 .…”

“…In general, hPSC‐organoids are extensively characterized using a broad range of experimental approaches, including transcriptomics, metabolomics, proteomics, single‐cell analysis, and microscopic imaging, which generate large and multimodal datasets that require further analysis and summarization 2,7 . However, current data analysis methods are highly inefficient because they require researchers to handle large volumes of data with varying levels of complexity manually or semi‐manually.…”

“…An organoid contains a complex multicellular cluster of organ-specific cells that closely resemble the structure and function of an organ. 1,2 Due to their striking similarity to organs, organoids have been proven to be beneficial for the study of organ development and human diseases. 3 Researchers are actively developing various organoids by creating an optimal environment with developmental-relevant biochemical and biophysical cues for stem cells to differentiate and resemble engineered tissues or organs with multiple cell types and intercellular crosstalk, thus increasing the predictive validity related to organ and tissue pathophysiology and function.…”

“…In general, hPSC-organoids are extensively characterized using a broad range of experimental approaches, including transcriptomics, metabolomics, proteomics, single-cell analysis, and microscopic imaging, which generate large and multimodal datasets that require further analysis and summarization. 2,7 However, current data analysis methods are highly inefficient because they require researchers to handle large volumes of data with varying levels of complexity manually or semi-manually. More importantly, as the volume and complexity of the data grow, the conclusions concerning the data become more difficult to obtain, 8 and human bias and intuition can lead to incorrect or contradictory conclusions in biomedical research and discovery.…”