Challenge. The genesis of cells as living drugs requires the development of technologies that can profile massive numbers of cells at single-cell resolution. Legacy live-cell imaging platforms evaluate cells in bulk, providing average data with few readouts that are predominantly focused on target cells. Advances in physical and information sciences have driven significant improvements in single-cell analysis, but these are constrained by modalities that evaluate cells at a single point in time—and many are cell destructive. To advance the next generation of cell‑based products, we require the ability to profile individual cell‑cell interactions and performance at scale—and retrieve cells for downstream analysis.
Solution. CellChorus places combinations of effector cells and target cells in each of thousands of individual microwells. CellChorus then applies Time‑lapse Imaging Microscopy in Nanowell Grids (TIMING™) with neural network-based artificial intelligence (AI) to identify and track cells, and to characterize their behaviors and interactions to produce a wealth of functional data. This provides high-throughput, single-cell analysis of cells and cell-cell interactions over time and produces data on migration, contact dynamics, killing, survival, cytokine secretion and sub-cellular activity/trafficking. TIMING has been featured in more than 20 peer-reviewed publications across multiple therapeutic types, including cell therapies (e.g., CAR T and NK cells) and antibodies—and is applicable to a wide range of disease areas, including oncology, infectious diseases, and autoimmune disorders. See example videos at cellchorus.com/videos.
Value to Customers. CellChorus already has customers in preclinical and clinical development, and manufacturing. This demonstrates that TIMING impacts the full life cycle of novel therapeutics, including:
• Understanding MOA based on function
• Prioritizing candidates for clinical studies
• Understanding patient response on clinical trials
• Maintaining consistency of manufacturing
• Predicting response to therapy to reduce non-responders and adverse events