By Jill Pease
A statistical method developed by researchers at the University of Florida College of Public Health and Health Professions can help scientists understand the mechanisms behind disease progression and identify new targets for therapies.
Scientists who use the method, called scLANE, will be able to characterize how individual genes behave during the core processes of cell development, including growth, adaptation and sometimes, malfunction. Unlike previous methods, scLANE is designed so results are easy to interpret. The software package also offers a comprehensive suite of downstream analysis and data visualization tools.
“Modern single-cell technologies allow scientists to reconstruct how cells change over biological processes, but identifying which genes drive these changes has been surprisingly hard,” said Rhonda Bacher, Ph.D., an associate professor in the Department of Biostatistics and scLANE’s principal investigator. “Existing methods use complex models that are difficult to interpret, leaving researchers to rely on subjective visual inspection. This new statistical approach keeps the flexibility needed for complex gene behavior while remaining easy to understand and explain.”
Development of scLANE was supported by a NIH Maximizing Investigators’ Research Award Bacher received from the National Institute of General Medical Sciences.
Lead author John “Jack” Leary, a Ph.D. candidate in biostatistics, drove scLANE’s design, analysis and validation since first exploring the idea as a master’s student.
“One of the primary motivators behind our work was wanting to understand disease progression and how cells develop while organisms are growing,” Leary said. “With our method you can pick out which genes are ‘driving’ the biological process. And if you understand better who’s behind the wheel and you need to change that process in some way, you have a better idea of how to do it.”
For example, a tool like scLANE can make it easier for scientists studying cancer to identify candidates for therapies that target tumor growth or boost the body’s immune response.
“Gene dynamics over time are super complex,” Leary said. “We built this tool so it could handle the complexity while still making sense from a biological perspective.”
In a paper published in the journal Nucleic Acids Research, Leary, Bacher and UF alumna Xiaoru Dong, Ph.D., now a research assistant professor at Texas A&M University College of Dentistry, describe validating scLANE’s accuracy with several simulations. One of their main case studies involved a type of white blood cell known as B-cell, in the human fetal liver. An analysis by scLANE of their dataset helped the team identify multiple genes involved in cell development that had been overlooked by comparable methods.
The software package is now available on the widely-used repository Bioconductor and is able to handle complex, multi-sample experimental designs. For users with less experience in coding, the team collaborated with UF’s HiPerGator software engineering team to develop a web server that runs on the supercomputer’s high-performance resources. Researchers can securely upload their data for analysis and receive an email when their results are ready for download along with instructions for using scLANE’s visualization tools.
The method is not limited to human applications. Early scLANE adopters have included plant scientists studying how bean roots develop over time, and biologists examining jellyfish evolution.
For Leary, scLANE has been a labor of love, involving many late nights and weekends perfecting the method, the software and user experience.
“I tried to make it as simple as possible so that it would be accessible to people who might have only a little bit of coding experience,” Leary said. “It’s exciting to have it published and out there for people to use.”