Ask More Of The Genotype
Jeanie Wolaver, VP of System Quality & Development
For a long time, a genotype was a yes or no. Is the gene there or not. That answer was enough when the models were simpler and the questions were simpler.
Automation solved that version of the problem. The routine call is fast, reproducible, and made the same way every time, whether it is the first sample of the day or the ten thousandth. That is settled ground.
What is not settled is everything the yes or no answer never captured.
Leaky cre expression. Mosaicism. Off target alleles. Copy number. Zygosity in transgenic lines. These questions can be consequential for interpreting the model. They are the cases where an animal is not what the paperwork says it is, and where a study can run for months on a foundation that was wrong from the first cage card. Researchers usually find out downstream, when results will not reproduce and nobody can say why. Sometimes they never find out at all.
Those are the problems worth solving, and they are what we are working on right now. Quantitative assays that resolve transgenic zygosity instead of inferring it. Copy number as a reported result rather than an assumption. Methods to surface mosaic and leaky signal instead of averaging it away. Detection of integration where it was not intended. The goal is to answer more of those questions from the sample already being submitted, before the animal enters a study.
That is what the Lab of the Future looks like from where I sit. Not a faster answer to the old question. A better question, answered early enough to matter.
Precision at the front of a workflow is the cheapest precision available. Everything downstream inherits it, and everything downstream pays when it is missing.
Characterize & Monitor The Model
Nate Nowak, VP of Scientific Affairs | Genetic Monitoring
The "Lab of the Future" will be defined by its operational efficiency rather than its physical appearance. While animal facilities and labs may look similar to today, they will feature increased automation, sensors, and smart caging. The most significant shift will be in research infrastructure: a streamlined environment where comprehensive data on models are available at the touch of a button.
Research teams will be free to focus entirely on experimentation and discovery. Centralized animal facilities and institutional staff will assume oversight of research models, acting as the resident experts in husbandry, genetics, and breeding. This can give research labs more confidence in the models they use.
This operational division directly addresses research rigor and reproducibility. Standardized facility practices can improve the consistency of research records. Robust administrative systems and automated workflows are just as critical as the hypotheses being tested. Specialized facility expertise can help identify sources of variation and strengthen the foundation of a study.
Current Laboratory Operations | The Lab of the Future |
|---|---|
Distributed Responsibility: Research labs split time between science and model maintenance. | Specialized Oversight: Centralized facility experts manage breeding, genetics, and husbandry. |
Manual Tracking: Reliance on routine genotyping and legacy documentation methods. | Automated Precision: Continuous 24/7 digital monitoring and precise, high-throughput genetic testing |
Variable Reproducibility: Data integrity is vulnerable to inconsistencies and variation in models. | Increased Rigor: Standardized quality control helps identify genetic variation, reduce operational waste, and inform animal-use decisions. |
Genetic Quality Control In Practice
Making this shift requires laboratory operations to keep pace with advancing technology. This demands an openness to investing in modern workflows, such as specialized colony maintenance software and advanced genetic screening. While these tools may have higher upfront costs, they improve adherence to the 3Rs framework (Replacement, Reduction, and Refinement), reducing animal numbers and eliminating waste.
Transparency is the cornerstone of this evolution. True reproducibility requires exhaustive documentation of every experimental variable. Modern technology makes it easy and affordable to automatically measure, document, and archive these metrics, moving past the limitations of manual recording and the current, high cost of molecular genetics work.
Over the next five to ten years, broader genetic characterization may become more accessible alongside routine targeted tests. Methods such as long-read sequencing could answer questions that targeted assays alone cannot. Combined with 24/7 non-invasive data collection that removes the variable of human handling, research teams may be better equipped to monitor model quality over time.
Bridging the gap to this “one test for all” future requires leveraging current diagnostic tools. Advanced genetic monitoring platforms, like the miniMUGA platform, are essential to this transition. By providing facilities with the capabilities needed for strict genetic quality control of genetically modified models, these technologies ensure a strong foundation to build on for biomedical research.
Continued development of nonanimal models, such as organoids and organs on a chip, may reduce reliance on animal models for some research questions. However, these models will still be important to basic research and discovery.
This means that it will be vital to ensure that the animal models that are being used are of high quality and yield rigorous and reproducible results. Characterization and genetic quality control are cornerstone practices in modern biomedical research utilizing these models. Without rigorous quality control, subtle genetic shifts or unverified modifications can introduce confounding variables, compromise reproducibility, and lead to erroneous scientific conclusions.
A primary focus of genetic quality control is managing the background genetics of the mouse strain. Inbred strains are engineered to be genetically identical, helping limit background variation in phenotypic responses. Mixed genetic backgrounds or variability within research models or their controls can confound experimental results and lead to models that cannot be genetically reproduced or maintained over time.
Regular background characterization using single nucleotide polymorphism (SNP) panels or short tandem repeat (STR) profiling ensures that the genetic foundation of the study remains stable and consistent across different laboratories.
Beyond managing genetic drift, rigorous validation is critical when working with genetically modified models, such as transgenic, knockout, or CRISPR-edited mice. When a gene is inserted, deleted, or modified, researchers may need to confirm the intended edit and assess possible unintended genomic changes. This confirmation requires a multi-step verification process:
Genotyping by PCR: Amplifies specific DNA segments to confirm the presence or absence of the target gene.
Sequencing: Verifies the precise nucleotide sequence at the edit site to ensure no insertion or deletion errors occurred.
Copy number analysis: Estimates the number of transgene copies, which can affect gene expression.
Off-target assessment: Uses methods appropriate to the edit and model to investigate possible unintended changes.
Confirming the precise modification is essential because a gene edit can inadvertently disrupt neighboring genes, or integrate into a transcriptionally silent region of the genome, leading to unexpected phenotypes. Furthermore, verifying that the modification is stably inherited across generations is vital for long-term study viability. By strictly validating both the genetic background and the specific gene edits, researchers safeguard the precision of their models. This diligent oversight helps researchers assess whether the targeted genetic manipulation explains an observed outcome, supporting more reliable interpretation of the resulting data.
Improvements in the scale, cost and efficiency of long read sequencing, dense SNP arrays, and quantitative PCR has made rigorous quality control and documentation much more affordable and efficient at the lab scale. What had been available only to larger breeders in the past is now easily within reach of all research labs. This has lead to the publication of numerous guidelines that lay out best practices for model characterization on quality control.
Treat The Microbiome As A Variable
Samira Lawton, Director of Product, Sequencing | Microbiome
The microbiome has been the confounding variable nobody wants to deal with. Too dynamic, too messy, too easy to punt on until a study doesn't replicate and someone's stuck asking why. The Lab of the Future doesn't ignore it anymore - reproducibility depends on treating the Microbiome like the real variable it is, not an afterthought.
This is the problem we've spent years chipping away at. We took something that used to require a dedicated biostatistician and a lot of patience, and turned it into something a researcher can just... do. Send samples, get answers, and move on with the actual science. I don't think the future of research is adding more tasks to the lab’s plate. Instead, we're removing the guesswork that used to sit hidden inside their data. Researchers get their time back, and the science that comes out the other side is stronger for it. That's the piece I care about getting right.
At AALAS 2026, explore how diagnostics can give research teams more context for the models behind their studies.
Genetic Monitoring Source References
Pardo-Manuel de Villena, F., Amos-Landgraf, J. M., & Bell, T. A. (2026). Improve genetic quality control to increase rigor and reproducibility of mouse research. This policy study used the MiniMUGA (Mouse Universal Genotyping Array) to reveal that nearly half of laboratory mouse strains had genetic discrepancies from their reported identities. The authors advocate for rigorous genomic validation to combat the hidden variations that destroy reproducibility.
The Laboratory Animal Genetic Reporting (LAG-R) guidelines are a newly established international framework specifically designed to address incomplete genetic reporting in animal research
FELASA Working Group. (2020). Genetic quality assurance and genetic monitoring of laboratory mice and rats. Published by the Federation of European Laboratory Animal Science Associations, this serves as the foundational text for standardized Quality Assurance (QA) protocols. It outlines best practices for using single nucleotide polymorphism (SNP) markers and short tandem repeats (STRs) to detect background genetic contamination.
Yoshiki, A., et al. (2022). Genetic quality: a complex issue for experimental study reproducibility. This review highlights testing regimens for genetically altered lines, detailing how in-house genotyping must confirm mutant alleles and monitor background strains to ensure data consistency.
Nature Lab Animal. (2017). Genetic drift: the ghost in the genome. This article addresses how independent mouse colonies accumulate spontaneous mutations over time. It explains why hidden genetic drift creates different substrains across vendors, which acts as a major confounding variable in preclinical research.
Jackson Laboratory White Paper (2025). Genetic Drift Occurs in Any Independent Mouse Breeding Colony and Can Impact Experimental Results. This repository report defines how to manage colonies carefully to minimize baseline divergence and properly use complex strain nomenclature.
UC Davis Mouse Biology Program (MBP). Genetic Integrity Resources. A framework detailing operational workflows for baseline genotyping, post-cryopreservation validation, and copy number variant (CNV) analysis to safeguard engineered CRISPR/knockout mouse lines. View the documentation at MBP Genetic Integrity.