Modern day drug discovery is shifting from single end point assays to more complex assays that query single cell and population responses to chemicals and genetic manipulation. One such assay, cell painting, is designed to use imaging to highlight cellular substructures and, via image analysis pipelines, quantify changes in the cellular state. This type of analysis often requires powerful computational resources and results in very large datasets that are difficult to interpret easily at the individual cell level, resulting in data averaging that can obscure the underlying heterogeneity of cell population responses.
A new open-source image analysis platform, called SPACe (Swift Phenotypic Analysis of Cells), described in Nature Communications and developed by researchers at Baylor College of Medicine, Texas A&M University and the University of Houston, will now provide researchers with a powerful tool to analyze these large datasets in an efficient way, while including evaluation of diverse single cell responses among heterogeneous populations.
SPACe supports large imaging data analysis on standard computers
SPACe addresses a significant limitation facing academic labs and small institutions: the computational resources required to analyze large volumes of imaging data.
"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 , professor of molecular and cellular biology and director of the Gulf Coast Consortium Center for Advanced Microscopy and Image Informatics co-located 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."
While pharmaceutical companies have historically relied on highly powered cloud computing systems to analyze these data, the new platform is designed to be accessible even to researchers using standard computers, lowering the barriers to entry for sophisticated cellular analysis.
SPACe offers a more nuanced understanding of how drugs interact with thousands of cells
At its core, the platform improves existing methods by enabling the analysis of thousands of individual cells generated by increasingly faster automated imaging platforms, and better capturing the variability of biological processes. This innovation enables a more nuanced understanding of how drugs interact with cells, revealing insights into mechanisms beyond cell death, such as changes in the characteristics of a cell and its individual internal structures (such as the nucleus, nucleolus, mitochondria, cytoskeleton and the cytoplasm). Collectively, this additional information adds an important, expanded dimension that can facilitate an increased understanding of a drug's mechanism of action.
"The platform allows for the identification of non-toxic effects of drugs, such as alterations in cell shape or effects on specific internal structures, which are often overlooked by traditional assays that focus largely on cell viability," said Dr. Fabio Stossi , currently a senior scientist with St. Jude Children's Research Hospital, the lead author formerly with Baylor during the development of this platform.
Stossi added that the SPACe can analyze thousands of cells, allowing for large-scale drug screenings on a standard computer, which makes this process available to laboratories of varying sizes and helps more researchers work together.
"This tool could be a game-changer in how we understand cellular biology and discover new drugs," Stossi said. "By capturing the full complexity of cellular responses, we are opening new doors for drug discovery that go beyond toxicity."
"The platform incorporates state-of-the-art routines for cell detection and feature extraction that was implemented in Python, ensuring high computational efficiency, portability and additional flexibility," said Dr. Demetrio Labate of the University of Houston.
Researchers interested in using the platform can access it through Github at https://github.com/dlabate/SPACe . The team plans to continue expanding its capabilities through collaborations with other institutions and research centers.
Others who contributed to the research and development of SPACe 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 and Demetrio Labate. They are affiliated with Baylor College of Medicine, Texas A&M University or University of Houston.
Software development, experimental approaches and imaging for this project were supported by the GCC Center for Advanced Microscopy and Image Informatics (CAMII, Cancer Prevention and Research Institute of Texas (CPRIT) RP170719), the Integrated Microscopy Core at Baylor College of Medicine (funding from National Institutes of Health grants DK56338, CA125123, ES030285, 699 S10OD030414) and CPRIT grant RR200043.
