Understanding Omics

Understanding Omics

How this new frontier is advancing physiological research.
By Dara Chadwick

If you’ve ever stood at the ocean’s shore, experiencing awe at its vastness while feeling the sand shift beneath your feet, you understand how physiologists working in omics sometimes feel. During the past two decades, new technologies have opened ever-changing pathways to vast quantities of molecular measurements and spawned a new era in physiological research.

“Omics” is the suffix used to describe scientific approaches that incorporate technologies like high-throughput sequencing and mass spectrometry to amass huge, searchable pools of data. Using omics, researchers can explore precise hypotheses by zeroing in on specific biological systems and cells. They can also follow their curiosity by querying and analyzing volumes of data to see where patterns and trends emerge.

“Omics can be an incredible amount of information about one person, or it can be a whole bunch of information about many people,” says Jeremy Prokop, PhD, data science adviser at Corewell Health in Grand Rapids, Michigan, and associate professor at Michigan State University College of Human Medicine. “Anything you can measure in a system now, you can look at in bulk and we call that ‘omic.’”

But incorporating omics isn’t as simple as opening a laptop and accessing a database for most physiologists, says Dennis Brown, PhD, FAPS, professor of medicine at Harvard Medical School and APS chief science adviser. “These giant datasets use sophisticated techniques, algorithms and artificial intelligence,” he says. “You need experts in the field to guide you.”

Where can physiologists find the data science expertise needed to embark on an omics approach? Start by exploring data science resources close to home. “You don’t have to start from scratch,” says Jasmine Plummer, PhD, director of the Center for Spatial Omics at St. Jude Children’s Research Hospital in Memphis, Tennessee. “Dig into what’s available in your community and apply physiology to it. Lean heavily on fields where you can find collaborators, but also work within your own university or academic environment.”

Harnessing Big Data’s Promise

There’s no denying the potential of omics. As an example, Prokop cites the whole genome’s role in exploring rare diseases. “We have 2 million individuals who have had their genome sequenced to date. I can look through all the genomes to see where variants occur commonly,” he says. “Then, I can take your individual genome, scan it and see a change you have that we don’t see commonly in the others, which might be the explanation of a rare disease.”

These data are transforming personalized medicine. “As physiologists, we embrace phenotypic measurements like blood pressure, temperature and cytokine levels,” he says. “Now, we’re talking about incredible scale of medical record integrations. I’ve got a genome, a transcriptome, knowledge of your viruses and your clinical records for 20 years. As we look at these omic datasets, we start to find signals that converge, such as inflammatory states or certain gene signals.”

These convergent signals tell a story, Prokop says. “AI and big data give us the ability to see how signals converge. Think of it like sonar: One ping doesn’t really inform me about something, but multiple pings let me track movement. With omics, we have the potential to see all these incredible signals and how they merge.”

Omics can also offer new cellular views. Gina Yosten, PhD, professor of pharmacology and physiology at Saint Louis University School of Medicine, uses spatial transcriptomics and spatial proteomics to study G-protein coupled receptors (GPCRs). Using spatial omics, which has emerged during the past five years, she and her team can understand how a cell’s transcriptotype translates to its function in three-dimensional space. 

“Often in anatomical studies, such as when we’re staining with antibodies, we’re looking at two dimensions. We’re looking at just one slice of tissue and losing a lot of information,” she says. “When we apply these large datasets to anatomy, we get all these rich data about where messenger RNA molecules are within a cell, which is important because location dictates function. We have access to techniques that tell us which cell contains each transcript and where it’s located within the cell.”

Ultimately, spatial omics could allow researchers to generate precise hypotheses about how the body may respond to different stressors. “It could tell us how cells might respond to different drugs, depending on their genetics and their transcriptotype,” Yosten says.

Plummer uses single cell spatial omics to examine why cells move from a normal state to a disease state. She began her career in bulk genomics before moving into using technologies like RNA sequencing and ChIP-Seq—a combination of chromatin immunoprecipitation (ChIP) assays with sequencing—to perform epigenomic profiling. 

“What we realized is that when you mush up an entire piece of tissue, what you get is a mixed signal,” she says. “I was an early adopter of single cell omic biology. We took the same genomic techniques—bar coding and using a sequencer—and added new molecular tools to isolate the cells.”

At St. Jude, Plummer and her team use spatial omics to examine isolated cells in situ to determine their function within a spatial context, as well as interactions between cells. “Maybe cells that are in the right place at the right time move along a normal trajectory,” she says. “But if a cell is in the wrong place at the wrong time, maybe that moves you into a path of disease. Most diseases are within a tissue. I think a lot about how we’re good at diagnosing cancer, but we can’t link it to outcomes. You can diagnose a certain type of cancer and put a child on a certain type of treatment. One child might do amazing on that treatment and another child might not.”

Differences in treatment response may occur because one cell has escaped treatment, she says. Identifying that rogue cell is where she sees the potential of spatial omics. “What was the surrounding environment that allowed that cell to escape?” she says. “What was it encased by?” Understanding the spatial context of cells may hold a critical key, she says. 

Hilary Coller, PhD, professor of molecular, cell and developmental biology at the University of California, Los Angeles, and editor-in-chief of Physiological Genomics, uses next-generation and high-throughput approaches to understand how cells transition between proliferation and quiescence. “This transition is a critical aspect of normal physiology, and we think it’s involved in pathophysiology,” she says. She and her team have used mass spectrometry metabolomics to understand changes in cells’ metabolism as they become quiescent. They’ve also used proteomics to examine what happens to chromatin when fibroblasts transition from proliferating to quiescent states. 

Understanding this transition could help improve our clinical knowledge of how to treat cancer and chronic wounds. Coller credits omics with advancing this work. “There’s been so much we’ve discovered that we just weren’t going to figure out with hypothesis-based testing,” she says. “There was no way I was going to be able to guess which genes went up with quiescence. It’s a small fraction of the whole genome. Only when you see them in aggregate can you start to tease out patterns and consistencies.”

Managing the Challenges 

For all their research promise, omics aren’t without challenges. One such challenge is data standardization. “If you’re going to try to merge data generated by different people, there are some potential problems,” Coller says. “There are stories about people thinking they saw something or that they had a great effect.”

Coller shares an anecdote she heard about a research project in which the controls were run on one day and the experimental samples were run on a different day. While the differences between the experimental samples and the controls were found to be a result of day-to-day variability in technical procedures rather than differences between the experimental and control samples, hearing that story was an important reminder of the importance of rigor in experimental design, Coller says—especially when data are included in a common dataset.

In large datasets, methodological standardization across experiments isn’t possible, according to Yosten. 

“An animal in my lab is not the same as an animal in someone else’s lab, even if it’s the same species, age and sex,” she says. “It’s a living body of work and as we expand our datasets, we can capture the remarkable genetic diversity present within the human species.”

What’s more important to think about is standardizing how researchers share information—for example, by providing rich annotation. Yosten says that rather than simply sharing raw RNA transcript counts, for example, researchers should also share detailed information about the source of tissue samples, how cells were collected and which platform was used.

Another challenge of omics is the at-times overwhelming volume of available data. “These technologies are superb, but that’s only half the story,” Brown says. “Physiologists need to use these big datasets, even if we don’t generate them ourselves, to interrogate physiological processes. That can be a daunting task because there are now hundreds of variations in identified genes. What is biologically significant? People are realizing that you need creative, independent physiologists to interpret these data at the cellular, organ and whole-body levels.” 

Yosten encourages physiologists to use omics data for  directed and undirected discovery. “You can test a hypothesis based on data you already have, and you can use it to confirm what you think is happening,” she says. “You can also let the data determine the direction. You can use an unbiased analysis and see what emerges.”

What’s Next? 

As new technologies and artificial intelligence change physiological research, it’s natural to wonder how the physiologist’s role may change. Prokop says physiologists are more important than ever in the omics era. 

This article was originally published in the January 2025 issue of The Physiologist Magazine. Copyright © 2025 by the American Physiological Society. Send questions or comments to tphysmag@physiology.org.