Project description:NCOMMS-24-54326: Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appears limited. In this study, we used several artificial intelligence techniques and patient-based multi-omic data to show that Ectonucleotide Pyrophosphatase/Phosphodiesterase 1 (ENPP1), which hydrolyzes STING-activating cyclic GMP-AMP (cGAMP), is a safer and more effective STING-modulating target than direct STING agonism in multiple solid tumors. We then leveraged our generative chemistry artificial intelligence-based drug design platform to facilitate the design of ISM5939, an orally bioavailable ENPP1-selective inhibitor capable of stabilizing extracellular cGAMP and activating bystander antigen-presenting cells without inducing either toxic inflammatory cytokine release or tumor-infiltrating T-cell death. In murine syngeneic models across cancer types, ISM5939 synergizes with targeting the PD-1/PD-L1 axis and genotoxic chemotherapy in suppressing tumor growth with good tolerance. Our findings provide new evidence supporting ENPP1 as a novel innate immune checkpoint across solid tumors and reports the first AI design-aided ENPP1 inhibitor, ISM5939, as a cutting-edge STING modulator for cancer therapy, paving a new path for immunotherapy advancements.
Project description:Type 2 Diabetic Nephropathy (T2DN), in the setting of type 2 diabetes, is the world占쎌뀼 leading cause of chronic kidney disease and end-stage kidney disease (ESKD). The increasing prevalence and heterogeneous phenotype of T2DN complicate the approach to treating patients. While kidney biopsy is the gold standard for exclusion of non-DN diagnoses and confirming diagnosis of DN, it is imperfect in predicting progression to ESKD. Artificial intelligence (AI) has the potential to improve classification of T2DN, predict progression risk, and via integration with urinary proteomic profiles identify novel urinary biomarkers, taken together augmenting and going beyond current pathology practice.
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
Project description:To identify heifers of contrasting fertility, serial rounds of artificial insemination (AI) were conducted in 201 synchronized crossbred beef heifers. The heifers were then fertility classified based on number of pregnancies detected on day 35 in the four AI opportunities. The reproductive tracts of a subset of the classified heifers were obtained on day 14 of the estrous cycle. Microarray analysis revealed differences in the endometrial transcriptome based on fertility classification.
Project description:Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.