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Dating back more than a century, biobanks have outgrown their beginnings as small, local collections to become large, global facilities that store and handle millions of samples and serve thousands of researchers at any given time. Over the years, biobanks have transformed from passive repositories into active research infrastructures that are increasingly bridging the gap between medical research and clinical applications.

“Today’s biobanks have evolved far beyond sample storage,” said Yan Zhang, PhD, president of proteomic sciences at Thermo Fisher Scientific. “They are automated, digitally connected systems integrated with hospitals and health networks to ensure appropriate consent, longitudinal clinical context, and the ability to re-engage participants over time.”

As safeguards of clinical samples, biobanks fulfill a central role in the advancement of precision medicine. Access to the right samples can make or break a research project, with most researchers reporting that they have had to limit their scope of work because of difficulties obtaining the samples they need.

“Robust, population-scale biobanking enables precision medicine to move from isolated findings toward broader clinical relevance,” said Zhang. “Modern biobanks combine genomics, proteomics, and other high-dimensional omics platforms with robust data architecture, high-performance computing, and artificial intelligence (AI)-driven modeling. Dedicated data science teams integrate molecular data, longitudinal health records, and curated public datasets to generate biologically meaningful interpretations.”

Biobanks now provide the infrastructure needed to support population-scale, longitudinal studies that allow scientists to uncover molecular drivers of disease and understand their evolution over time to ultimately identify biomarkers, develop targeted treatments, and inform clinical decisions.

“We’re seeing researchers design studies with scale in mind,” Zhang noted. “They’re combining proteomics, genomics, and clinical data to generate insights that are both statistically powerful and relevant to real-world populations. There’s also a clear shift from searching for a single biomarker to building a more complete, systems-level understanding of disease.”

To navigate today’s rapidly shifting landscape and meet their core purpose of supporting cutting-edge clinical research, biobanks have to keep up with fast-moving targets. Going forward, moving from initial discovery to translation will remain the number one challenge in precision medicine. “Generating discovery insight is no longer the limiting factor,” said Zhang. “Validating, standardizing, and implementing those insights at scale is.”

A matter of scale

One of the most transformative shifts in biobanking over the past decade has been an exponential increase in the scale of data collection and sample storage. At the forefront of this expansion is the UK Biobank, which currently stores around 18 million samples from 500,000 participants, together with imaging and biomarker data, healthcare records, questionnaires, physical measurements, demographics, lifestyle, and environmental data collected over the course of 20 years. This depth of phenotyping is what makes the data so valuable to researchers worldwide, said Martin K. Rutter, MD, professor of cardiometabolic medicine at the University of Manchester and deputy chief scientist at the UK Biobank. “When you link all that together, you can get amazing insights into the biology of disease.”

To keep up with increasing storage needs and researcher requests, the UK Biobank is now getting ready to move more than 10 million samples currently stored in its main laboratory to a new building in central Manchester by the end of the year. The new storage facility is designed to quadruple sample retrieval speed while making the whole infrastructure more energy-efficient and environmentally friendly.

The scale at which facilities like the UK Biobank operate today would have been unthinkable when it was established two decades ago. Such massive growth has been driven by rapid technological advances across genomics, transcriptomics, and proteomics, with costs continuing to fall while coverage, speed, and accuracy keep surging.

Partnerships with the pharmaceutical industry have also been instrumental in nurturing this exponential growth. This can be seen in initiatives like the UK Biobank Pharma Proteomics Project (UKB-PPP), a collaboration between the UK Biobank and 14 biopharmaceutical companies with the goal of analyzing proteomics data from 600,000 samples.

In the long run, scale provides the backbone to enable increasingly ambitious, statistically powerful studies. However, as they grow, biobanks face the challenge of navigating a constantly shifting landscape while making sure the samples and data they collect, store, and maintain are valuable to the entire research community they serve.

“Our job is to make the data available to researchers,” said Rutter. “We are involved now more than ever in connecting with research teams and trying to understand what their needs are.”

Through surveys and consultations, the UK Biobank actively gathers information to design prospective data collection programs that anticipate researcher needs. Next year, the biobank is planning a repeat assessment of its whole cohort, focusing on measurements of aging. The goal is to support researchers looking into causal pathways and mechanisms driving age-related diseases, empowering the development of preventive interventions and new diagnostics and treatments for age-related conditions.

Keeping pace with the evolving demands of researchers, industry, and the broader public is essential for biobanks to secure the funding necessary not only to operate but also to expand such vast enterprises, which remains a major challenge across this resource-intensive field.

Diversity takes the spotlight

Historically, samples collected by biobanks are biased in favor of participants who are white, middle-class, and have a higher education. This creates major disparities in the applicability of clinical research. In fact, studies have shown that patients from non-European ancestry backgrounds have not benefited equally from precision drugs approved by the U.S. Food and Drug Administration (FDA) to treat a range of cancer indications.

Even within biobanks dedicated to sampling the population of a specific region, ethnic minorities, low-income, or elderly people are often underrepresented, skewing results against the real-world populations they strive to serve. As the research community increasingly recognizes the importance of more diverse and representative patient cohorts, demand is rising for resources that address these barriers.

Representation is at the heart of All of Us, a program launched by the National Institutes of Health in 2018 to address the gap present at the time in many biobanks and sample repositories. This precision medicine initiative was designed to enroll participants who reflect the full range of populations found within the U.S., including individuals of varied ancestry backgrounds as well as those living in rural commmunities, which are rarely represented in biorepositories due in part to longstanding barriers to research participation, such as the logistical challenges of collecting samples and data from participants in remote locations.

“A lack of diversity impoverishes discovery and applicability of findings for all,” said Joshua C. Denny, MD, CEO of the All of Us Research Program.

For instance, data collected by All of Us has been used to investigate APOL1 gene variants linked to kidney disease, which are more common among people of West African ancestry. This research led to the identification of a novel APOL1 variant that can reduce the risk of kidney disease in individuals carrying high-risk variants.

The program has so far enrolled about 870,000 participants across all U.S. states, with about 80% of them representing communities that have historically been underrepresented in biomedical research. This has been achieved by emphasizing accessibility and flexible participation models; participants can enroll digitally and choose whether to share access to their electronic health records, donate biospecimens, and complete demographics and lifestyle surveys. They may also opt to provide saliva samples, simplifying logistics in rural areas with limited access to blood collection facilities.

“What works in a rural location is different from what works in a big city like New York,” said Denny. Whether it comes to location, age, or language, he emphasized the importance of adapting how the program approaches and engages each population.

Democratizing access to patient data across the research ecosystem is another major biobanking challenge that All of Us is committed to addressing. The program has established a streamlined access model that enables researchers to access the data they need in less than two hours if they belong to one of the 1,300 already approved institutions across the world. Together with central data storage and cloud-based analysis tools, their setup is designed to make the data accessible to researchers lacking the resources and local infrastructure for high-performance computing.

Towards global integration

With precision medicine studies steadily escalating both in size and complexity, researchers increasingly seek to bring together data stored across diverse biobanks to power larger, more ambitious studies with broader scientific and societal impact. However, building the infrastructure needed to enable cross-biobank studies is still a challenge, starting with convening stakeholders to harmonize data collection standards and establish international guidelines.

Anticipating this need, in 2013 the European Union established the Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium (BBMRI-ERIC), which currently coordinates the activity of about 500 biobanks across 32 countries.

“Precision medicine can only move forward with a strong starting point for research,” said Jens K. Habermann, MD, PhD, professor for translational surgical oncology and biobanking at the University of Lübeck and director general of the BBMRI-ERIC. “It can be very difficult for scientists to get all the information they need in one place, and this is what biobanks can enable.”

Pulling together data from all its members, the BBMRI-ERIC has set up a central catalogue for biobanks, biomolecular resources, and other data and sample collections, which users can employ to identify relevant resources and build virtual cohorts tailored to their research needs. The consortium also works with international committees to set guidelines and support members working towards compliance with international standards.

Despite ongoing progress, there are still obstacles ahead when it comes to harmonizing biobanking practices worldwide, including data collection, annotation, storage, and sharing. Tackling differences in data protection, consent, ethical standards, and regulatory requirements across borders will be another necessary step towards broader standardization. Finally, biobanks will need to invest in cybersecurity to ensure patient data can be shared between institutions safely.

Funding will be key to successfully addressing all these challenges. On this front, biobanks face the difficult task of maintaining their existing infrastructure, staying up to date and relevant to the research community, and investing in cross-biobank initiatives. All this must be balanced with growing financial pressure on research centers, hospitals, and the governments supporting them.

As part of its 10-year roadmap, the BBMRI-ERIC is setting the goal of forming international networks that bring together more diverse biobank types, such as environmental, wildlife, veterinary, and plant biodiversity repositories. The overarching aim is to move towards a One Health approach to biobanking, where samples and data that expand beyond monitoring human populations are brought together to tackle overlapping challenges that simultaneously affect human, animal, and environmental health.

Data-driven horizons

As the field forges ahead, biobanks are undergoing broad transformations in the way they operate. On the technology side, these changes are being propelled by the rise of multi-omics techniques in precision medicine research, as well as by rising demand from the research community for non-invasive patient monitoring data and longitudinal sample collection. All of these will be critical for the development of the next generation of personalized therapies and diagnostics.

“Over the next decade, biobanks are expected to become increasingly integrated into clinical and translational workflows,” said Zhang. “Proteomics, in particular, will play a growing role in helping us understand the dynamic biology of disease, enabling earlier detection, better prediction of recurrence, and more precise therapeutic strategies.”

A key driver of this shift will be AI. No longer just a supporting tool, AI is now becoming an integral part of biobank operations, contributing to real-time sample monitoring, predictive maintenance, risk management, and decision making.

On the data analysis side, Zhang has seen how AI is redirecting the focus from data generation to data interpretation. She said, “Biobanking has already enabled the collection of high-quality biospecimens linked to large-scale molecular and clinical datasets. The challenge now is extracting meaningful biological insight from that complexity.”

Although still in its early days, AI is becoming central to how researchers make use of biobank data, noted Rutter. Drawing from the UK Biobank data, recent studies have developed AI models that can predict a patient’s risk of stroke based on retinal images, calculate the risk of future disease by looking at an individual’s disease history, or spot neurodegenerative diseases like Alzheimer’s and Parkinson’s early using brain scans and physical activity data.

Going forward, Rutter expects to see biobanks moving away from static cohorts and in favor of continuous data collection, enabling more powerful predictions. For example, the UK Biobank is developing a mobile app that can track a participant’s physical activity and monitor their location and sleep patterns, offering an in-depth look at how a variety of factors affect their health with much more accuracy than self-reported surveys.

Over time, all these advances will steer clinical practice from treatment to prevention, allowing healthcare professionals to act early in the patient journey, when interventions are most effective, and eventually, even before disease develops. Ultimately, addressing complex diseases will require coordinated contributions from all stakeholders, including AI innovators, drug developers, clinicians, technology providers, and policymakers.

“The next decade will be incredibly exciting,” said Denny. “It will be all about leveraging the huge scale of resources that are just emerging today.”

Clara Rodríguez Fernández is a science journalist specializing in biotechnology, medicine, deeptech, and startup innovation. She previously worked as a reporter at Sifted and editor at Labiotech, and she holds an MRes degree in bioengineering from Imperial College London.

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