What could annual check ups look like in 25 years? Longitudinal analyses of your historical health data to detect early trends, and AI-powered algorithms used to compare biological profiles to that of other patients, grouping individuals into molecular categories for tailored treatment.
Imagine a world where a simple drop of blood, a finger-prick, or a wearable sensor provides a continuous readout of your biological health – without having to visit the hospital. Where AI-powered tools analyze not just your DNA, but your entire molecular profile, to predict risks, guide personalized treatments, and even prevent diseases from developing at all. What would it take to make this vision a reality? What role will analytical science play? And can we get there by 2050?
Here, as part of a series of articles tackling these important questions, we speak with Ying Ge, Vilas Distinguished Achievement Professor, Department of Cell and Regenerative Biology, Department of Chemistry, and Director of the Human Proteomics Program, University of Wisconsin-Madison, USA.
What does precision – or personalized – medicine mean? And how can analytical science contribute to a more precise approach to healthcare?
In clinical practice, we have long relied on a “one-size-fits-all” approach – treating patients with the same medications and dosages, regardless of individual differences. I once heard a nurse jokingly describe some medications are like "horse pills" – everyone gets the same dose, regardless of their individual characteristics. That’s a problem because people have different genetic, biochemical, and physiological profiles, and a single treatment isn’t going to be equally effective for everyone.
This is where the concept of personalized medicine emerged – the idea that each person is unique and should have an individualized treatment. While the idea of personalized medicine is compelling, it’s not yet practical to develop a completely unique treatment for each individual. Hence, precision medicine comes in – a more feasible strategy that involves grouping patients with similar characteristics and treating them with tailored therapies.
A great example from my own research – our PNAS paper looked at obstructive hypertrophic cardiomyopathy (HCM) patients. It is a fairly common genetic heart disease and a leading cause of sudden cardiac death in young adults. While HCM is known to be caused by mutations in sarcomeric protein genes, these genetic differences alone do not reliably predict clinical outcomes. So, we used high-resolution mass spectrometry-based top-down proteomics to comprehensively characterize sarcomeric proteoforms in septal myectomy tissues from obstructive HCM patients. We observed a complex landscape of sarcomeric proteoforms shaped by combinatorial PTMs, alternative splicing, and genetic variation in HCM. Surprisingly, we found a shared pattern of altered sarcomeric proteoforms across these patients, regardless of their specific mutations. It was really the direct evidence showing that proteoforms can better reflect patient’s disease phenotypes than their genotypes. This is how analytical chemistry contributes to precision medicine – helping identify biochemical differences that standard genetics or clinical markers might miss.
Genomics is, of course, an essential tool in medicine, but it only gives part of the picture. How do we actually understand patient differences at a deeper level? That’s where we need advanced analytical technologies – mass spectrometry, proteomics, metabolomics – to provide more comprehensive patient data.
I recently attended the American Society for Clinical Chemistry (AACC) conference. It was fascinating to see how rapidly the field of clinical chemistry is evolving and keeps expanding its role in clinical diagnostics and precision medicine. I presented our work on top-down proteomics, and it received a lot of attention, which shows just how much interest there is in bringing analytical chemistry into precision medicine.
Do we need new tools or is it a question of better integrating the tools we do have into clinical practice?
It’s both. The tools we have today can already be applied to precision medicine. For example, with top-down proteomics and multi-omics, we can characterize patients at a deep molecular level. But we also need to do a better job of integrating these tools into the clinical setting.
One example is our Nature Communications paper on troponin I, which is the gold standard biomarker for heart attacks. Typically, current troponin immunoassays use antibodies to measure total troponin levels in the blood. Although these immunoassays are highly sensitive, they can’t distinguish between different proteoforms of troponin – which could offer more insight into the type or severity of heart damage. In contrast, top-down proteomics allows us to see proteoforms in detail, which serve as molecular fingerprints with high precision and resolution.
In theory, we could use proteoform signatures to better categorize patients and guide treatment decisions. This is especially important because there’s a gap in cardiac diagnostics using high sensitivity cardiac troponin immunoassays, sometimes elevated troponin levels don’t necessarily mean a heart attack. These patients often undergo unnecessary hospitalization and invasive procedures, which add costs and risks. We hope to bridge this gap with nano-proteomics – using mass spectrometry-based assays to improve accuracy and specificity beyond immunoassays.
Mass spectrometry really embodies what precision medicine is all about – precision, accuracy, sensitivity, and resolution – qualities that make it a powerful tool for precision medicine. My PhD advisor, Fred Lafferty, used to say that mass spectrometry has transformed scientific disciplines over the past 50 years and he was excited to see how it would keep evolving. We’re looking forward to seeing how it will continue to improve in the next 50 – bringing it from the bench to the bedside.
On the tools side of the equation, what are the main areas that need improvement?
One of the biggest hurdles is throughput. Right now, personalized proteomics isn’t fully achievable because of limitations in throughput and proteome coverage. In genomics, we’ve reached a point where personal genome sequencing is widely accessible. But sequencing the full proteome – including all proteoforms – is still a major challenge.
That’s one of the goals of the Human Proteoform Project, a major initiative led by Neil Kelleher and the Consortium for Top-Down Proteomics. The vision is to establish a comprehensive catalog of proteoforms, similar to how personalized genomics has mapped the genome. Hopefully, one day, a personalized proteoform project will be a reality, but we're not there yet.
The key challenges are:
Throughput – we need higher throughput methods to analyze large numbers of proteoforms efficiently
Sensitivity – especially in single-cell analysis, we are still far from the high sensitivity achieved in single-cell transcriptomics
Coverage – right now, top-down proteomics is powerful but still faces limitations in the total number of proteoforms we can confidently identify and quantify.
For example, in our PNAS paper, we demonstrated top-down proteomics in multinucleated single muscle cells, showing that we can extract valuable biological information from a single cell. But throughput, sensitivity, and proteome coverage need to be further improved.
That said, we don’t need to wait for all these challenges to be solved before applying current technologies. We can use existing technology to tackle biological and clinical problems now, while simultaneously working on improving the tools.
I’ve been impressed by how clinical chemists and pathologists are already starting to use top-down proteomics in clinical diagnostics. That’s proof that while we still have a long way to go, the technology is already making an impact. But of course, we need to keep pushing forward to refine and enhance these methods.
What’s your vision for precision medicine in 2050 – or even further into the future?
Right now, when you visit a doctor, the amount of real-time data collected is very minimal – blood pressure, heart rate, maybe a few blood tests. But in the future? That’s going to change dramatically.
I envision a world where you walk into a clinic, and instead of a basic checkup, you’re greeted by a large dynamic digital display that compiles years of your personal health data – genomics, proteomics, metabolomics, microbiome – and more, all in real time.
Imagine a world where your annual exams wouldn’t just be routine, superficial checkups, but comprehensive, data-driven evaluations. Your longitudinal health data (from previous visits) would be analyzed continuously to detect early trends. AI-powered machine learning algorithms would compare your molecular profile to millions of other patients, grouping individuals into precise molecular subtypes rather than just broad disease categories.
One idea that excites me is the concept of “Molecular Twins” – something I first heard Jenny Van Eyk present in one HUPO conference. The idea is that as soon as you show early signs of disease, your data would be matched to another patient with a similar molecular profile. If that “molecular twin” responded well to a certain treatment, doctors could apply the same approach to you – dramatically improving the efficiency and success rate of personalized treatments.
In 50 years, I believe this will be routine. Cancer detection will no longer rely on late-stage diagnoses because we’ll have the tools to track its emergence at the molecular level. We won’t be “blind” to disease progression; we’ll be able to intervene much earlier. Diseases that are currently “invisible” will become detectable, predictable, preventable, and treatable before they take hold.
Currently, much of medicine is reactive – clinicians wait for symptoms to manifest, often at advanced stages of disease before taking actions. In the future, proactive and preventative medicine will be the norm, driven by big data, machine learning, and real-time molecular insights.
I truly believe that the integration of analytical science, precision diagnostics, and AI-driven healthcare will completely transform medicine – making it predictive, personalized, and profoundly more effective.
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