Modern drug discovery is evolving from simple assays to more complex phenotypic assays that examine single-cell and population responses to chemicals and genetic changes. Cell painting, a type of assay, uses imaging to highlight cellular substructures and quantify changes in cellular states through image analysis pipelines. This process often generates large datasets that are challenging to interpret at the individual cell level, leading to data averaging that can obscure the heterogeneity of cell population responses.
Researchers from Baylor College of Medicine, Texas A&M University, and the University of Houston have developed an open-source image analysis platform called SPACe (Swift Phenotypic Analysis of Cells). This tool aims to help researchers analyze large datasets efficiently while evaluating diverse single-cell responses among heterogeneous populations.
“The pharmaceutical industry has been accustomed to simplifying complex data into single metrics. This platform allows us to shift away from that approach and instead capture the full diversity of cellular responses, providing richer, more informative data that can reveal new avenues for drug development,” said Dr. Michael Mancini. He is a professor of molecular and cellular biology and director of the Gulf Coast Consortium Center for Advanced Microscopy and Image Informatics at Baylor College of Medicine and TAMU Institute for Bioscience and Technology. “This new platform is open-source and available to anyone. We see this impacting both academic and pharmaceutical research communities.”
The platform is designed to be accessible even on standard computers, making sophisticated cellular analysis available without high-powered cloud computing systems typically used by pharmaceutical companies.
At its core, SPACe enhances existing methods by analyzing thousands of individual cells generated by fast automated imaging platforms. It captures biological variability more effectively, allowing a deeper understanding of drug interactions with cells beyond just cell death effects.
“The platform allows for the identification of non-toxic effects of drugs, such as alterations in cell shape or effects on specific organelles, which are often overlooked by traditional assays that focus largely on cell viability,” said Dr. Fabio Stossi, a senior scientist with St. Jude Children’s Research Hospital who was formerly with Baylor during the development of this platform.
Stossi added that SPACe enables large-scale drug screenings on standard computers, facilitating collaboration among laboratories regardless of size.
"This tool could be a game-changer in how we understand cellular biology and discover new drugs,” Stossi stated. “By capturing the full complexity of cellular responses, we are opening new doors for drug discovery that go beyond toxicity.”
Demetrio Labate from the University of Houston noted: “The platform incorporates state-of-the-art routines for cell detection and feature extraction implemented in Python, ensuring high computational efficiency, portability and additional flexibility.”
Researchers interested in using SPACe can access it through Github at https://github.com/dlabate/SPACe. The team plans further enhancements through collaborations with other institutions.
For more information about accessing the platform or reading the published paper visit https://www.nature.com/articles/s41467-024-54264-4.
Contributors to SPACe's research include Pankaj K. Singh, Michela Marini, Kazem Safari, Adam T. Szafran, Alejandra Rivera Tostado, Christopher D. Candler, Maureen G. Mancini, Elina A. Mosa, Michael J. Bolt along with Demetrio Labate from Baylor College of Medicine or Texas A&M University or University of Houston.
The project received support from GCC Center for Advanced Microscopy and Image Informatics (CAMII) funded by CPRIT RP170719 as well as NIH funding (DK56338 CA125123 ES030285 699 S10OD030414) CPRIT RR200043 through Integrated Microscopy Core at Baylor College Medicine.
