Genetically Diverse Cell Lines Are Key to More Inclusive Disease Modeling

Cell lines play a vital role in disease modeling and drug discovery, providing a cost-effective, easy-to-use, and unlimited supply of material for biomedical research. Unfortunately, the cell lines commonly used today often fail to capture humanity's genetic diversity.

It is estimated that only 5% of cell lines in commonly used panels are of African descent, with the majority originating from Europeans. This lack of diversity can lead to an incomplete understanding of diseases and, ultimately, to a biased approach to drug discovery.

“To take data generated from cells with very similar genetic ancestries and treat it as generalizable for anyone with the same disease can have dangerous consequences, not only for biasing future studies of that disease, but also for developing treatments that only help a subset of patients or even may harm others,” Dr. Raeka Aiyar, executive director of the Institute for Glial Sciences at Case Western Reserve University School of Medicine, told Technology Networks.

“I often think about this in the context of the BMI [body mass index] scale,” added Dr. Erika Moore, assistant professor in the Fischell Department of Bioengineering at the University of Maryland, College Park. “It is widely used as a standard, yet it was originally developed based on a very specific population, Belgian men. That raises an important question: why do we continue to treat such limited frameworks as universal? The same issue exists in biomedical research when we rely on unrepresentative models.”

Genetic diversity is becoming an increasingly important consideration in experimental design, with pharma regulators such as the US Food and Drug Administration deprioritizing animal testing in favor of stem cell models. Additionally, as the National Institutes of Health (NIH) invests more heavily in new approach methodologies and stem cell models become more centrally used in biomedical research, incorporating genetic diversity will be key to ensuring research benefits all of society.

What is meant by “diversity” in cell lines?

Diversity, when describing cell lines, refers to the inclusion of models derived from genetically varied ancestry. Many diseases display substantial genetic heterogeneity, meaning that specific risk variants may function differently in patients with varying genetic backgrounds, leading to different phenotypes and responses to medication.

For example, research has shown that ethnic minorities are at greater risk of dementia. The use of diverse cell lines is therefore paramount to understanding how an individual's genetic background could influence dementia progression.

Whilst the first human cell line, HeLa, came from an African-American patient, Henrietta Lacks, today most cell lines are from patients of European descent. One study found that several cancer cell lines labeled as mixed or black/African-American had even been misclassified.

Moore argues that disease models should reflect the diversity within the disease population. “This includes not only sex, but also genetic ancestry and other relevant biological variables. The goal would be to ensure that our foundational understanding of cell function is built on models that reflect the people we aim to serve.”

When studying diverse cell lines, Aiyar highlights the importance of differentiating between genetic ancestry and race.

“Race is a social construct, not a biological one,” said Aiyar. “What we get when we use more diverse cell lines is a more complete picture of the spectrum of human biology, and better information about which genetic factors lead to which kinds of phenotypes. Some of those genetic factors may be more prevalent in certain populations, but that doesn't mean they allow us to put those populations into neat little boxes.”

When models fail: Consequences of non-diverse cell lines

The importance of diversity in study design is illustrated by the anticoagulant warfarin. The drug can cause hemorrhaging that was linked to certain genetic variants found to be more common in African populations, a factor that wasn’t sufficiently considered in the preclinical or clinical study design of the drug.

“Pharmacogenomics has given us countless more examples: 67% of Puerto Rican children do not respond to the drugs used in asthma inhalers; up to 75% of Pacific Islanders are unable to convert the antiplatelet drug clopidogrel into its active form and have a higher risk for adverse outcomes from angioplasty, commonly used in cardiovascular disorders,” explained Aiyar.

Genome-wide association studies, which researchers use to identify genetic variants associated with a particular disease, are estimated to include 78% of participants of European ancestry. “This means that our picture of the genetic architecture of these diseases is incomplete at best and incorrect at worst,” Aiyar said.

The inertia of the “reference” cell line

Unrepresentative cell lines have remained the default in research, partly due to a lack of incentive to change the status quo. Moore argues that there is a longstanding tendency in science to rely on established systems and that unrepresentative cell lines persist “largely because they are accessible and considered well-characterized.”

Additionally, “most labs aren’t equipped to assay large panels of cell lines,” stated Aiyar. As a result, the field has chosen a select few “reference” cell lines, which are often of European ancestry. These same cell lines then dominate peer review, grant review, and reference datasets, making them difficult to change.

“This isn't just in cell biology—the Human Genome Project used a mixed sample from five men of European ancestry, and moving beyond that ‘reference genome’ took many years,” Aiyar said.

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A lack of samples from minoritized communities also contributes to underrepresentation, Aiyar explains: “Many minoritized communities are hesitant to donate their samples to biomedical research, which is entirely justifiable given the history of exploitation and abuse they have endured at the hands of our scientific predecessors.”

Labs hoping to investigate more diverse panels can struggle to access lines from diverse ancestries, samples with which to generate those lines, or ways to engage those communities to obtain the samples. “Cell villages are one way around this, but they require significant sequencing costs and analytical expertise that again are not always accessible,” stated Aiyar.

Moore believes that until funding agencies and institutions more actively challenge these norms, there will be limited incentive for labs to adopt more inclusive approaches.

“The NIH mandate on sex as a biological variable is a strong example,” said Moore. The mandate, which went into effect in January 2016, set out the NIH's expectation that scientists account for the possible role of sex as a biological variable in vertebrate animal and human studies.

The Office of Research on Women's Health at the NIH published a framework in 2023 for complying with sex-inclusive mandates, organizing their recommendations within the “4 Cs of Studying Sex to Strengthen Science”, defined as the following:

• Consideration: Consider the impact of sex on the phenomenon under investigation and commit to an exploratory or confirmatory approach to sex differences.
• Collection: Collection and tabulation of sex-based data.
• Characterization: Analysis of sex-based data.
• Communication: Reporting and interpreting sex-based data.

“Before that policy, many researchers simply did not consider sex in their experimental design,” said Moore.

Despite the policy coming into effect over a decade ago, a review of 574 NIH-funded publications from 2017–2024 found that while 61% of studies included both sexes, only 44% analyzed results by sex, even when both sexes were included. The authors of the review suggest that stronger guidance and incentives are needed to ensure sex is fully considered in biomedical research.

What would a better cell line development system look like?

In an ideal system, biology-based comparisons would be integrated from the outset with representation intentionally built into the experimental design.

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“A practical starting point is to ensure that disease models reflect the populations most affected by the disease. This requires asking simple but often overlooked questions: who is most impacted by this condition, both in the US and globally, and how is that reflected in the models we use?” said Moore.

Access to ancestrally diverse cell lines remains challenging. Aiyar envisions that centralized repositories of ancestrally diverse lines hosted by institutions worldwide could help overcome this.

“Cell lines of all kinds should be generated in highly standardized ways and subjected to rigorous quality control, otherwise technical variation will obscure the biology,” Aiyar said. “Access to accompanying genetic and genomic datasets that can be integrated into downstream phenotyping efforts would further increase the value of these centralized repositories.”

Partnerships between organizations such as the Allen Institute for Cell Science and the New York Stem Cell Foundation (NYSCF), which Aiyar helped initiate while at NYSCF, hope to meet this need. In 2025, the two organizations announced a partnership to produce fluorescently tagged, ethnically diverse stem cell lines for disease research. By combining the Allen Institute for Cell Science’s structure tags and the stem cell automation technology from NYSCF (which has since been acquired by Jackson Laboratories), the partnership hopes to remove the hurdles for labs wanting to transition to ethnically diverse stem cells.

Further to improving accessibility, Aiyar believes that partnerships with the communities that scientists hope to serve will be vital. “Rebuilding the trust that our scientific predecessors destroyed will not happen overnight; it will require sustained engagement, dialogue, and partnership. It will, in fact, require entirely different models of how human biology research is done, including reimagining the role of the ‘subjects’ as true partners.

“…I feel it is part of our responsibility as scientists to remember that we are at the service of society and not vice versa, and to approach our research accordingly,” said Aiyar.

Changing the foundations of disease modeling

Precision medicine cannot succeed without the effective incorporation of cell line diversity into study design. To achieve this, Aiyar explained that certain barriers still need to be overcome: “If we want to expand precision medicine research that examines disease phenotypes and drug responses at population scale, we need better technology for parallel analyses of cell line panels. This is underway but not yet accessible to most labs.”

As precision medicine advances, the use of diverse cell lines is vital for ensuring that communities underrepresented in disease research can benefit. Better, more diverse disease models will ultimately help improve global health equity.

To this end, the International Society for Stem Cell Research (ISSCR) recently launched the Consortium on Advanced Stem Cell-Based Models in Drug Discovery and Development. The consortium, of which Aiyar is a member, aims to define standards and best practices for using iPSC models to predict drug responses, including integrating genetic diversity into these applications.

“When we make discoveries about disease or therapeutics, the standard should be to reproduce them in lines from more representative backgrounds before we announce them as generalizable findings. Making this into a standard means enforcing it throughout the field—in peer review, grant review, and consortia,” Aiyar concluded. “Global consortia like the ISSCR’s will play a key role in driving new norms for using stem cell models as a means to more inclusive precision medicine research.”