CRISPR SCREENS FOR TARGET IDENTIFICATION IN DRUG DISCOVERY
Pooled CRISPR screens are widely used to supercharge drug target identification and validation. At Myllia, we perform CRISPR-based functional genomics screens in primary human cells that capture physiologically relevant human disease biology. We enable drug target discovery at scale by linking CRISPR perturbation screens to various high-content phenotypic read-outs such as fitness, flow cytometry, and single-cell RNA sequencing (CROP-Seq) - paving the way for next-generation drug targets addressing unmet medical needs in immunology and oncology.
Drug target ID/validation incl. deconvolution and identification of disease-associated gene function using functional genomics
Drug target ID/validation incl. deconvolution and identification of disease-associated gene function using functional genomics
Target identification is the first step of a drug discovery campaign and begins with high-content screening to identify possible ‘druggable’ targets and their role in the respective disease. Myllia’s unique CROP-Seq (Perturb-Seq) technology platform enables phenotypic screens in the best available cellular models, thus supporting the identification and validation of critical genes or pathways that drive disease.
Mode of action (MoA) analysis and high-content screening for drug signatures
Mode of action (MoA) analysis and high-content screening for drug signatures
Understanding how drugs act in the complex environment of a cell remains one of the critical aspects of drug discovery and development. Importantly, phenotypic screening with CROP-Seq delivers transcriptional profiles associated with drug action and indicates which gene knockouts affect the drug profile, thus providing unique insights into its mechanism of action. It also uncovers genes that modify drug responses, thus paving the way for patient stratification as well as the development of combination therapy approaches.
CROP-Seq perturbation datasets for training of AI/ML-based foundation models of human cells
CROP-Seq perturbation datasets for training of AI/ML-based foundation models of human cells
Human cells have proven very useful for drug target identification, yet many cells are not accessible to wet-lab experimentation in vitro. What if we could build computer models to predict how cells behave? We believe that such Foundation Models of Human Cells could become fantastically useful. However, we lack large scale cause-effect datasets to train, fine-tune and test such models.
At Myllia, we perform CRISPR perturbation screens at unprecedented scale and combine these with high-content read-outs, such as single-cell RNA sequencing. Such datasets are ideally suited to train AI/ML models of human cells as they provide causal links between the CRISPR perturbation and the downstream effects and provides insight into the inner workings of the cell.
Pooled CRISPR screens in primary human immune cells
Pooled CRISPR screens in primary human immune cells
Any CRISPR screen is only as good as the cellular model that is used. Therefore, we strive to use the most predictive cellular models and utilize primary human cells whenever we can. We have ample experience in culturing and manipulating primary human T cells. This includes the introduction of the CRISPR machinery as well as the annotation of different T cell subsets. In addition, we are extending our platform to other primary human cells, including myeloid cells and epithelial cells.