Sharing over 95% of its genes with humans and exhibiting fundamental similarities in physiology and anatomy, the mouse serves as a powerful model for deciphering human health and disease 8 .
Imagine a world where we understand the exact function of every gene in the mammalian genome, and can use this knowledge to develop cures for thousands of human diseases. This is not science fiction; it is the ambitious goal of functional genomics, and the humble laboratory mouse is the key to making it a reality.
Gene similarity between mice and humans
Protein-coding genes in the mouse genome
Driving this global endeavor is a massive European-led scientific infrastructure. Through coordinated networks and consortia, European scientists are systematically investigating the mouse genome, generating invaluable resources that are accelerating biomedical research across the globe.
Functional genomics moves beyond simply listing the genes in an organism's DNA (its genome). It seeks to understand what each gene does—how it functions, what proteins it produces, and how it interacts with other genes and the environment.
The most direct way to understand a gene's function is to see what happens when it is switched off. This is the core principle behind large-scale mouse phenotyping.
The International Mouse Phenotyping Consortium (IMPC) is a global collaboration of scientists from 21 research institutions with a monumental mission: to determine the function of every protein-coding gene in the mouse genome 3 .
Scientists systematically "knock out" or deactivate each of the roughly 20,000 mouse genes 3 5 .
The mice with these knocked-out genes then undergo a battery of standardized physiological tests, known as phenotyping. These tests screen for changes across a wide range of biological systems, from metabolism and neurology to cardiovascular function and development 3 8 .
All the resulting data on gene function is made freely available to the global research community via online databases 3 .
Europe plays a leading role in this global initiative through the INFRAFRONTIER research infrastructure 8 . This pan-European network brings together leading mouse clinics and archives, such as the European Mouse Mutant Archive (EMMA), to form a seamless pipeline for discovery and distribution.
They act as a repository, cryopreserving thousands of unique mouse mutant strains to ensure these valuable models are not lost.
They distribute these mouse models to researchers worldwide upon request, fueling hypothesis-driven research into specific human diseases.
They continuously work on improving methods for phenotyping, cryopreservation, and data analysis, making the entire process more efficient and informative.
| Metric | Figure | Significance |
|---|---|---|
| Mouse mutant lines archived | 6,000+ lines (3rd largest global repository) | A vast, living library of genetic models for human disease 8 |
| Annual strain submissions | ~390 per year (2015-2022 average) | Continuous growth and diversification of the available resource 5 |
| Annual strain shipments | ~350 per year | High demand from the global research community 5 |
| Key allele types archived | Tm1a (knockout-first) and CRISPR-generated strains | Embraces both traditional and cutting-edge gene-editing technologies 5 |
While generating knockout mice is essential, understanding complex systems like the brain requires advanced tools to interpret the data. A groundbreaking study published in Nature Communications in 2025 showcases how European researchers are pushing these boundaries by combining functional genomics with artificial intelligence.
The brain is hierarchically organized into fine-grained regions, each with specialized functions. While projects like the Allen Brain Atlas have provided a reference map, the advent of spatial transcriptomics—a technology that measures gene expression across thousands of individual cells while retaining their spatial location—has generated datasets of unprecedented size and complexity.
The ABC-WMB dataset, for example, contains millions of cells, presenting a massive computational challenge for analysis .
To address this, researchers developed CellTransformer, a sophisticated AI model based on a transformer architecture (similar to that used in advanced language models). Its purpose is to perform data-driven discovery of fine-grained spatial domains in the mouse brain .
For each individual cell in the vast dataset, the model identifies its local "neighborhood"—all other cells within a specific physical distance .
CellTransformer is trained in a self-supervised way. It takes the gene expression and cell type data from all the cells in a neighborhood and uses it to predict the gene expression of the cell at the very center of that neighborhood .
Through this process, the model learns to create a compact numerical representation (an embedding) that encapsulates the unique molecular and cellular context of each neighborhood .
Finally, the model uses a clustering algorithm to group neighborhoods with similar embeddings, thereby identifying distinct, biologically relevant spatial domains without prior human labeling .
The results were striking. CellTransformer successfully processed multi-million cell datasets from several mouse brains and identified hundreds of coherent spatial domains .
The AI-generated maps were highly consistent with existing expert-drawn neuroanatomical atlases, confirming the method's accuracy .
More importantly, the model went beyond existing maps, identifying hundreds of previously uncataloged areas in regions of the brain that currently lack detailed subregion annotation, such as the superior colliculus and midbrain reticular nucleus .
| Finding | Description | Implication |
|---|---|---|
| Scalability | Processed a dataset of 9 million cells across 239 tissue sections. | Makes the analysis of organ-scale datasets computationally feasible. |
| Discovery Power | Identified hundreds of spatial domains not in existing brain atlases. | Opens the door to discovering new, functionally distinct brain areas. |
| Cross-Animal Integration | Achieved nearly perfect consistency of up to 100 domains across 4 different mice. | Ensures findings are reproducible and not unique to a single individual. |
| Multi-Modality | Successfully performed domain detection on Slide-seqV2 data, another spatial transcriptomics technology. | Shows the method is versatile and can be applied to various data types. |
This experiment highlights a powerful shift in functional genomics: from merely describing what happens when a gene is knocked out, to using AI to discover how genes orchestrate the complex spatial organization of tissues, revealing a new level of biological insight.
The progress in mouse functional genomics relies on a sophisticated set of tools and reagents. The following table details some of the key resources that empower researchers in this field.
| Tool / Resource | Function | Example / Application |
|---|---|---|
| CRISPR-Cas9 | A precise gene-editing system that acts as "molecular scissors" to cut DNA at specific locations, allowing genes to be knocked out or modified. | Used to generate Ym1-deficient mice on mixed genetic backgrounds, enabling precise study of its role in health and disease 4 . |
| Knockout First Alleles (Tm1a) | A specific type of genetic modification that allows researchers to conditionally disrupt a gene's function in a particular tissue or at a specific time. | A common allele type archived and distributed by repositories like EMMA, providing flexible research models 5 . |
| Lipid Nanoparticles (LNPs) | Tiny fat-based particles used as a delivery vehicle to transport CRISPR-Cas9 components or other genetic therapies into the cells of a living animal. | Crucial for in vivo gene editing; recent advances have improved their efficiency and targeting 7 . |
| Spatial Transcriptomics | Technologies that allow researchers to measure all gene activity in a tissue sample and map where that activity is occurring. | The foundation for the CellTransformer experiment, enabling AI-driven discovery of brain regions . |
| Standardized Phenotyping Pipelines | A set of uniform, systematic procedures for testing the physical characteristics of mutant mice across different research labs. | Ensures data from different institutions (like those in the IMPC) is comparable and reliable 8 . |
The integration of CRISPR gene editing, spatial transcriptomics, and AI analysis represents a powerful technological convergence that is accelerating discoveries in functional genomics.
The journey to understand the functional genome is a long one, but the path is clearer thanks to the coordinated, large-scale efforts led by European science. Through the INFRAFRONTIER network and its central role in the International Mouse Phenotyping Consortium, Europe is not only managing an indispensable repository of biological models but also generating the foundational knowledge that will drive medical advances for decades to come.
The integration of disruptive technologies like CRISPR for precise gene editing and AI for data analysis is accelerating this progress, transforming the mouse from a simple model organism into a high-resolution blueprint for understanding human biology.
Each knockout mouse strain archived and each gene function deciphered brings us one step closer to unlocking the mysteries of genetic diseases and developing the next generation of personalized therapies.
The quiet work happening in labs across Europe, centered on a creature small enough to fit in the palm of your hand, is fundamentally shaping the future of human medicine.