Researchers at the University of Chicago's Pritzker School of Molecular Engineering (UChicago PME), led by Associate Professor Nicolas Chevrier's group, have developed a groundbreaking new system. This innovation allows for the mapping of gene expression across entire mouse body sections, directly addressing the previous limitation of gene expression studies often confined to single organs or small tissue areas.
"The tool allows for the generation of datasets at a previously unavailable scale, laying a foundation for a 'virtual mouse' that could test therapies and facilitate the understanding of body-wide biological processes."
The interdisciplinary work, spearheaded by staff scientist Maggie Clevenger, involved a collaborative effort between industrial and academic partners. The new technique seamlessly integrates advanced specimen preparation methods with sophisticated computational tools, including a crucial machine learning model, to meticulously analyze tissue.
The system has demonstrated remarkable accuracy, successfully mapping all organs, tissue regions, and approximately 75% of known cell types within the mouse body. These significant findings, recently published in the journal Cell, provide a comprehensive toolkit for researchers. This toolkit is poised to revolutionize the study of molecular and cellular processes across the entire laboratory mouse body, with profound potential applications in both basic science research and drug discovery.
Leveraging Spatial Transcriptomics for Whole-Body Analysis
The technique at the heart of this innovation is spatial transcriptomics. This method combines high-resolution microscopy with genetic sequencing to precisely measure gene expression across tissue sections. While spatial transcriptomics traditionally offers invaluable insight into organ and tissue structure and disease, its application was previously limited to small scales. Chevrier's team set out to overcome this challenge, aiming to apply it across an entire mouse model.
In 2025, the team developed Array-seq. To adapt Array-seq for comprehensive whole-mouse analysis, novel methods were meticulously developed. These included generating incredibly thin slices of frozen mouse bodies, precisely transferring them onto Array-seq slides, ensuring their integrity, and perfectly preserving RNA. This intricate process was achieved through a key collaboration with Professor Tadafumi Kawamoto of Tsurumi University, resulting in cross-sections comparable to the thickness of an average cell.
Advanced Computational and AI Models
Following the spatial transcriptomics phase, a sophisticated new computational model was developed specifically for cellular information annotation across the entire mouse. This critical development was a collaboration with Ashwini Patil of Combinatics. Additionally, in partnership with Professor Feng Bao of Fudan University, a machine learning model was created to accurately label organs, tissues, and cell types on standard hematoxylin and eosin-stained tissue sections.
Chevrier highlighted that this AI model enables virtual and cost-effective labeling, offering a significant advantage over labor-intensive manual laboratory methods.
Real-World Validation and Future Impact
The effectiveness of these integrated technologies was rigorously tested by measuring inflammation in a mouse model of sepsis. Chevrier reported that the system allowed for an unprecedented level of quantification: the impact of systemic inflammation on every cell type and major organ tissue was measured at a scale previously deemed impossible. This achievement enables molecular mapping of the laboratory mouse and other model systems at an advanced and comprehensive scale.
The new system holds wide-ranging applications, from studying how specific genes affect various areas of the body to thoroughly assessing the effects of new drugs, potentially revealing unpredicted tissue impacts. The subsequent goal for the team involves modeling the entire mouse body, a crucial step toward generating data that could ultimately contribute to creating a "virtual mouse" model for research. Chevrier suggested that this data could be an enabling technology for the long-envisioned "virtual laboratory mouse model."