Explorez tous les épisodes du podcast Mendelspod Podcast

Sometimes the best evidence that a new medical technology is working is simply that doctors start depending on it. For years, the promise of molecular medicine has been that better genomic information would lead to better patient care. At Roswell Park Comprehensive Cancer Center, Vice Chair of Molecular Pathology, Sean Glenn, offers a compelling example of that promise becoming routine clinical practice.

Hematologic cancers can require information from many different kinds of genomic alterations and traditionally from multiple tests and laboratories. Glenn and his team developed a comprehensive pan-hematological assay that brings much of that information together, using both DNA and RNA to detect alterations across myeloid and lymphoid cancers. The panel targets nearly 400 genes on the DNA side and about 125 on the RNA side, while automation has helped Roswell Park achieve an average turnaround time of roughly two and a half days for the 50 to 60 tests it runs each week.

And the oncologists are depending on it. Since introducing the panel, Glenn says Roswell Park has seen “a transformation and a resurgence of clinical care” in its leukemia and lymphoma clinics. His favorite measure of success is wonderfully practical. The oncologists are no longer calling the lab every few days asking where their results are. The information simply arrives in the patient’s record, in time to help make treatment decisions.

Plant biology too is benefiting from single cell resolution.

Unlike animals, plants cannot move away from environmental stress. They have evolved tremendous biological complexity to stay rooted in one place. Their genomes are complex too, with repeated genome duplications producing families of related genes that can take on different functions. And within a single leaf are many specialized cell types, from photosynthetic and epidermal cells to vasculature and rare trichomes, each with its own patterns of gene expression.

It’s early days for plant science. The field is only beginning to assemble the large, high quality single cell atlases needed to understand that complexity and eventually make better predictions and enable a new world of plant engineering.

Robin Buell and Anita Giabardo join us from the University of Georgia to discuss one of those new atlases. Their BioPoplar project aims to engineer fast growing poplar trees to produce new biomaterials, bioproducts and biofuels. Using Illumina’s PIPseq, Anita and colleagues created an atlas of more than 159,000 cells across the developing poplar shoot.

That resolution matters. The researchers were able to find rare trichome cells, the tiny hairs on leaves whose specialized metabolism makes them attractive little factories for engineered products. They then identified a promoter that can specifically direct gene expression to those cells.

Robin has watched plant genomics progress from Northern blots to bulk RNA sequencing and now to single cell. She predicts that within ten years single cell could become the standard. The ability to see biology cell by cell is not only changing what scientists can discover about plants. It will mostly likely change what they can build.

What new insights can we learn about cancer when researchers are able to look at the genome and the methylome together?

Recently, Illumina launched its 5-base for Cancer Research Grant Contest to explore that question. Today, the three winners join us on Mendelspod with three very different problems in cancer research.

Richard Tothill of the University of Melbourne is studying cancers of unknown primary and in collaboration with Hartwig Medical Foundation is combining DNA methylation and mutation profiling to reveal the primary origins of these aggressive cancers. Jacob Blum of Johns Hopkins is working with tiny amounts of circulating tumor DNA, combining genetic variants with methylation signals in hopes of improving detection of minimal residual disease and developing a model that other researchers could use. And Alfonso Bellacosa of Fox Chase Cancer Center is investigating what methylation can reveal about the mechanisms behind response and resistance to cancer immunotherapy.

The projects are early stage, but together they give us a snapshot of how cancer researchers are using a new tool that brings epigenetics and genetics into the same experiment and analysis. Joining the discussion is Danielle Goldberg from Illumina, who talks about what the company saw across nearly 200 grant applications and the growing interest in combining genomic and methylation data in cancer research.

A recent New York Times article highlighted a remarkable study showing that exercise can help keep colon cancer in remission. After eight years, 90 percent of patients in a structured exercise program were still alive, compared with 83 percent in the control group. Yet despite these results, such programs are rarely covered by insurance. It’s a striking example of something our guest today, Mayo Clinic physiologist Mike Joyner, has argued for years. Are we overlooking some of the most powerful ways to improve human health in our pursuit of molecular medicine?

Over a decade ago, we invited Joyner on as a skeptic of the emerging precision medicine revolution. He questioned whether breaking biology into ever smaller molecular pieces would deliver the improvements in human health being promised. Ten years later, we begin by asking Mike a simple question. Was he right? “I think I was more right than wrong,” he says. Despite important successes in oncology and drug development, he argues that precision medicine has yet to make the broad impact on public health its advocates envisioned.

Joyner remains as provocative as ever. He argues that biomedical researchers have become too dependent on engineered animal models and should pay more attention to nature’s own experiments. The discovery of GLP-1 drugs is a case in point. He welcomes their enormous potential for improving public health but worries that people taking them without exercising may lose valuable muscle mass and miss the additional benefits of physical activity.

We also venture into the contentious debate over transgender athletes in women’s sports. Drawing on physiological research, Joyner argues that suppressing testosterone does not fully eliminate the athletic advantages associated with male puberty. For him, the evidence supports maintaining biological sex categories to preserve fair competition for women.

Throughout the conversation, Joyner challenges us to think beyond the molecular and ask a bigger question. Are we doing the kind of science that will actually make people healthier?

Is biology headed back into the genome?

While much of biology has been expanding outward into single cells, spatial biology and ever more biological context, Žiga Avsec and his team at Google DeepMind are making the case that there is still an enormous amount to learn by going deeper into DNA itself.

Their new AlphaGenome Atlas uses AlphaGenome to predict the molecular effects of every possible single letter change in the human genome. That’s some nine billion variants. Now researchers can easily explore how a variant might affect gene expression, or splicing, or other layers of gene regulation across different cell types. The Atlas also introduces a variant impact score (AVI) designed to help researchers quickly identify which variants deserve a closer look.

Avsec argues that the scale itself opens new possibilities. Researchers can use the Atlas to prioritize rare variants across enormous cohorts, but they can also work backward from the predictions to investigate something more fundamental, the regulatory grammar of the genome. The grammar of the genome—long a provocative idea, but still not cracked. Short DNA motifs could act something like words, and AlphaGenome may help reveal how those words work together to activate or repress genes across different biological contexts.

There are important limitations. AlphaGenome predicts molecular consequences rather than phenotype, and Avsec says the distance between genotype and phenotype remains long and complex. The model also has more difficulty with regulatory elements that far from genes—a proximity issue—and has so far been trained largely on bulk tissue data rather than the much richer universe of individual cell types and cell states.

For now, Avsec just wants researchers to use the Atlas. The portal is free for noncommercial research, and he explicitly invites scientists to tell the DeepMind team what works and what does not. That feedback matters because the Atlas is not being presented as a finished map. It is part of a cycle in which better models suggest better experiments, those experiments generate better data, and better data produce the next generation of models. Bioinformaticians always want two things. Better data and more data.

This may be an argument for where genomics is headed. Rather than AI replacing experiments, Avsec hopes it will give researchers greater confidence about which experiments are worth doing and ultimately lead to more experiments with more positive findings.

The AlphaGenome Atlas is available free for noncommercial research here:

https://deepmind.google/science/alphagenome/

Mass spectrometry has been one of the most powerful technologies in clinical testing for decades. So why is it still largely confined to specialty labs?

Don Mason has spent more than 25 years in clinical mass spectrometry. Now Senior Marketing Manager for Mass Spectrometry at Roche Diagnostics, he says the technology’s strength has also been its weakness: “Part of its power and part of its challenges are linked.” Mass spec is extraordinarily sensitive, selective and flexible, but traditionally requires specialized operators, complicated workflows and batch processing.

That may finally be changing. Last year Roche launched their new cobas Mass Spec solution designed to automate the process from sample preparation through result reporting. It also brings random-access mass spectrometry into the routine clinical lab. Mason says a sample can now produce a numerical result in as little as 34 minutes, compared with turnaround times that can stretch into days with traditional workflows. He points to transplant drug monitoring and antibiotic and antifungal monitoring in critically ill patients as examples where that difference could matter clinically.

But automation isn’t simply about replacing expertise. Mason argues that moving established assays onto standardized systems could free mass spectrometrists to develop the next generation of tests: “This in turn becomes the innovation engine for tomorrow’s routine mass spectrometry-based tests.”

Roche currently has seven assays available in the U.S., with more expected beginning in 2027. Looking further ahead, Mason sees a much bigger transition. Just as MALDI-TOF transformed microbial identification, he predicts LC-MS will become standard equipment in more and more clinical laboratories over the coming decade.

Jennifer Dionne, Stanford physicist and co-founder of Pumpkinseed, joins us to talk about a radically different way of reading proteins. Pumpkinseed’s deSIPHR technology combines nanophotonics with Raman spectroscopy to detect the molecular vibrations of individual amino acids. The goal is de novo protein sequencing that can read not only the 20 canonical amino acids, but potentially the enormous alphabet created by post-translational modifications and other forms of protein variation.

Raman spectroscopy is super cool even if nearly a century old. Shine a laser on a molecule and a tiny fraction of the photons change color as they interact with the molecule’s vibrations, producing a characteristic molecular signature. The problem has always been sensitivity. As Dionne explains, only about one photon in a million undergoes this Raman scattering. Her work uses nanophotonics for specially patterned materials to amplify that faint signal by orders of magnitude.

Why does that matter? Mass spectrometry has been the workhorse of proteomics, but it loses much of the sample during ionization and generally depends on existing catalogs for identification. Pumpkinseed wants to read what is actually there, including proteins and modifications we may never have seen before.

One early application is particularly timely with the recent news of cancer vaccines. Working with Genentech, Pumpkinseed is studying immunopeptides, the protein fragments displayed on the surface of cells that allow the immune system to distinguish healthy from diseased tissue. Direct sequencing of these peptides is a way to improve personalized cancer vaccines by identifying the mutations actually present in an individual patient’s tumor.

Ultimately, the ambition is much larger. “We want to be able to sequence all of the proteins that are in individual cells,” Dionne says. For biology and for AI models trying to learn biology, she argues, we first need to learn how to read much more of its language.

Digital technology has been promising to transform pathology for years. But the last year looks different. Roche paid roughly $1 billion for PathAI. Tempus acquired Paige. Other deals are adding to a sudden wave of consolidation. And AstraZeneca is developing a computational pathology algorithm for TROP2 that could become a companion diagnostic used to determine which patients receive a drug.

DeciBio partner Katie Maloney says these are signs that digital pathology may finally be reaching an inflection point.

The important shift is from digital to computational pathology. Until recently, much of the value proposition was about making an existing workflow more efficient. Now algorithms are beginning to extract clinical information that a pathologist could not simply determine by eye. Maloney points to tools that can predict prognosis, stratify patients and potentially predict drug response. Computational pathology is beginning to compete with, and increasingly complement, molecular diagnostics.

The transition is still early. Maloney estimates that only 20 to 30 percent of US labs have adopted even a slide scanner. Reimbursement remains a major obstacle, with labs generally not paid for scanning slides, using image management software or deploying computational algorithms. And some of the hardest problems are surprisingly basic. Different labs stain the same tissue differently, creating variability that algorithms must accommodate if they are going to work across thousands of clinical sites.

But pharma may change the equation. Maloney is watching to see whether AstraZeneca’s work proves to be an isolated example or the beginning of something much larger. If computational pathology becomes important across a significant share of new drugs, particularly antibody drug conjugates, pathology images become another rich source of patient data that can be layered with clinical and molecular information. As Maloney puts it, computational pathology is becoming “not just a tool for pathologists, but it’s a precision medicine tool.”

It was the heady days of the CRISPR revolution and of the gene therapy Casgevy. And when longtime genomics editor and author Kevin Davies went looking for a book that told the story of sickle cell disease and could not find one, he was perplexed and then inspired.

A few years later, the result is Curved Air, a biography of sickle cell anemia that traces one of the most remarkable arcs in modern biology. Davies begins with Victoria Gray, the first sickle cell patient treated with the CRISPR therapy that became Casgevy. Her transformation leads him backward through more than a century of discovery, from the first description of sickled blood cells to the identification of sickle cell as the first molecular disease. He writes of the extraordinary biology of fetal hemoglobin that made today’s therapy possible.

This is also a story about the gap between biology and medicine. Davies explores the neglect and discrimination endured by sickle cell patients and the difficulty of bringing a multimillion dollar therapy to those who need it. With a list price of $2.2 million for Casgevy, there is an enormous challenge of extending this advanced therapy to the millions of patients around the world who are in need.

After being pursued for more than 150 years, cancer vaccines may finally be having their moment.

The recent positive Phase III results from Moderna and Merck offer what today’s guest Dr. Elias Sayour calls the first “bona fide evidence” that a therapeutic personalized cancer vaccine can work. For Sayour, a pediatric oncologist and cancer researcher at the University of Florida, the result is not the culmination of the field. It is “just the tip of the iceberg.”

Sayour explains why cancer has been such a difficult target for vaccines. Cancer is heterogeneous and constantly evolving. Yet the immune system evolves too. Sayour describes the contest as an “epic battle between an evolutionary foe and an evolutionary guardian.”

His own research points toward an intriguing next step. Sayour’s lab has found that an mRNA vaccine may not always need to carry a cancer specific target. Nonspecific mRNA can wake up a dormant immune response and potentially make any tumor more responsive to checkpoint inhibitors. Retrospective observations in cancer patients receiving COVID mRNA vaccines have strengthened that hypothesis, and Sayour says his group expects to begin a prospective clinical trial shortly.

The larger vision is striking. Sayour imagines combining universal immune activation, personalized vaccines and eventually therapies that anticipate where an evolving cancer is going next. After decades of frustration, cancer vaccines have finally delivered a major clinical success. The question now may be not whether they can work, but how far this new way of programming the immune system can take us with cancer and other diseases.

Single cell sequencing has given researchers extraordinary new maps of human biology. For Nick Banovich of TGen, the burning question is how to turn those maps into something that matters for patients.

Banovich has spent much of his career studying pulmonary fibrosis, and single cell sequencing has changed the field’s understanding of the disease. Instead of looking at an average signal from diseased lung tissue, researchers can now separate molecular changes from changes in the populations of cells themselves. That has helped point drug developers away from simply targeting fibrosis and toward earlier changes in epithelial and endothelial cells. Banovich sees spatial technologies as the next step.

Late in the conversation, Banovich tells of his group discovering a population of cells found almost exclusively in patients with pulmonary fibrosis, cells that had never been described before single cell sequencing. Later, using spatial transcriptomics, Banovich and his team were able to locate those same cells directly in diseased lung tissue, to physically see their finding.

We also discuss perturbation experiments, organoids, AI and virtual cells. Throughout the conversation, Banovich returns to the reason he came to TGen in the first place. Discovery is exciting, but ultimately he wants these technologies to affect disease and improve patient care.

Note: Nick will continue the conversation as a panelist in GenomeWeb’s virtual roundtable, “Single-cell Sequencing in the Era of Translational Medicine.”

Register for the GenomeWeb virtual roundtable

Stanford physicist and bioengineer Polly Fordyce has a big vision. She’s attempting to measure protein function at the scale at which we learned to measure DNA. We can sequence proteins. We can increasingly predict their structures. But we still have a surprisingly difficult time measuring what proteins actually do.

Fordyce wants to change that. Her lab is developing ways to measure protein folding, binding, kinetics and function at a massive scale, using the quantitative language of physics. Her ambition is not simply to create more protein data. She wants measurements good enough to make biology more predictive.

Her favorite analogy is weather forecasting. Better computers and better models helped transform our ability to predict the weather. But so did thousands of weather stations around the world making standardized measurements of temperature, wind and precipitation. Biology now has extraordinary computational power and increasingly powerful models. Fordyce thinks it needs the equivalent of those weather stations.

Last year, Schmidt Sciences awarded Fordyce a Polymath Award worth up to $2.5 million to pursue that idea. Her lab has developed a new bead based technology that could allow ordinary laboratories to make high throughput measurements of protein function. Fordyce hopes scientists around the world will contribute those measurements to a new open resource she calls the Functional Protein Observatory.

The implications go well beyond building a database. Fordyce describes recent work from her lab on a protein involved in cancer and developmental disease. After making hundreds of thousands of measurements across human variants, the researchers found that the prevailing model for how drugs act on the protein may be wrong. The drugs appeared to stabilize a previously unseen form of the protein that was only partially closed. That could help explain why drugs designed around the old model have struggled. And it shows what can be discovered when scientists measure how proteins actually behave rather than relying on a static picture of their structure.

AI makes the project more timely. Computational models can now propose proteins and mutations far faster than scientists can experimentally test them. Fordyce believes that gap can be closed. Her new platform can go from receiving a library of DNA to functional measurements within 72 hours. This opens up the possibility of a continuous cycle in which AI can propose, experiments test, and the measurements make the models better.

Mark Van Oene has just taken over as CEO of Pacific Biosciences, and Mendelspod is his first interview in the new job. He began his career as a scientist studying cystic fibrosis genetics at Toronto’s SickKids, then spent fifteen years at Illumina, rising from its first sales rep in Canada to Chief Commercial Officer. He joined PacBio in 2021 as COO and has spent the past five years helping build the product portfolio he now inherits as CEO.

He joins us today with a clear message: PacBio is choosing long reads. The company has moved on from its Onso short-read platform and is concentrating its resources on making HiFi sequencing cheaper, more scalable and the routine choice in certain clinical applications.

With the new SPRQ-Nx chemistry, PacBio has brought the list price of a HiFi whole genome down to $345. For Van Oene, that changes the problem. “It’s becoming less about economics for me right now.” The bigger constraint is scale, and PacBio expects a new high-throughput system next year.

The opportunities he keeps going to are in the clinic. In rare disease, long reads can consolidate multiple tests while revealing parts of the genome other approaches miss. He sees that eventually leading to long-read whole-genome sequencing at birth: “If you don’t know what you’re looking for, let’s use the most comprehensive analysis you can get.”

10x Genomics has been remarkably good at figuring out where biology is going next.

In this wide-ranging conversation, co-founder and CEO Serge Saxonov explains why. Despite decades of genomics, “we still understand very little biology.” For 10x, the way forward has been to build better tools for measuring it and to work backward from the biological questions rather than forward from a particular technology.

That philosophy helped drive the single-cell revolution and 10x’s move into spatial biology. Now the company has launched Atera, its new spatial platform designed to measure the whole transcriptome with single-cell sensitivity and at much greater scale. Customer shipments are expected to begin later this year. Saxonov argues that spatial is becoming something much bigger than another research niche. It’s a way of bringing molecules, cells and tissues into the same view.

Going beyond RNA? Saxonov discusses 10x’s recent moves into proteomics and why he believes binder-based approaches currently offer the best path to scale. And he says 10x is investing directly in clinical diagnostics, pointing to therapy selection in oncology and autoimmune disease as two early opportunities. Getting closer to patients, he says, should also help 10x understand what its next generation of technologies needs to do.

What would convince Saxonov that all this is succeeding? Not another instrument or another omic. It would be a measurable improvement in clinical-trial success.

“The next decade is going to be wild,” he says.

Those of us in biology tend to think that chemistry is rather straight forward. But today’s guest says there’s quite an art to synthesizing a molecule.

A few years ago, Lee Cronin, a chemistry professor at the University of Glasgow joined Mendelspod for a sprawling conversation about the origin of life, alien biology, assembly theory, and his conviction that chemistry could become programmable. Now Cronin is back, and that last idea has become a company that now has a “chemifarm.”

First let’s look at the problem. It is easy to assume that once a drug company has designed a promising molecule, the chemists more or less know how to make it. Not so. Chemistry remains surprisingly artisanal. A molecule that looks perfectly plausible on a computer may require too many steps to synthesize, depend on unstable intermediates, or require reactions that simply aren’t known. And chemists may not discover that until they’ve spent considerable time trying.

This becomes an even bigger problem in the age of AI. Algorithms can propose vast numbers of new molecules, but which ones can actually be made? And if one can’t, is there another molecule nearby in chemical space that could perform the same function but be dramatically easier to synthesize?

That’s the problem Chemify is trying to solve.

“You cannot discover what you cannot make,” Cronin says in today’s interview.

He spent 15 years developing χDL, a programming language that reduces the work of chemistry to a set of basic operations that machines can execute. Chemify’s software can then work backward from a desired molecule to determine a possible route for making it. Its robotic systems execute those instructions in the physical world, including chemistry requiring unusual temperatures and conditions. And its Chemifarms bring large numbers of these systems together so the process can be repeated at scale.

The result is something Cronin calls a chemistry “hyperscaler.” A pharmaceutical company might bring Chemify a molecule proposed by its own AI system. Chemify can ask whether that molecule is realistically makeable, develop a route to it, physically attempt the synthesis, and feed what happened back into the system. If the molecule isn’t practical, the platform can suggest alternatives. Every success—and importantly, every failure—adds information about what parts of chemical space are actually accessible.

Cronin’s ambition is enormous. He wants Chemify to become a sort of utility, the “AWS for all drug discovery companies.” Not another company competing to discover the next drug, but infrastructure that allows pharmaceutical and AI companies to turn digital ideas into physical matter.

There are very few genomics projects with the level of ambition of the NIH All of Us Research Program. The latest release includes data from more than 747,000 participants, 535,000 whole genomes, 480,000 electronic health records, and—for the first time—long-read sequencing, proteomics, transcriptomics, and a large collection of information extracted from clinical notes. More than 24,000 researchers are now using the resource. But the program is also arriving at an important transition: roughly 80 percent of its original ten-year funding runs out this year.

So what happens when a massive national research experiment begins to come of age?

Josh Denny, CEO of All of Us, joins us to talk about what the program has accomplished, what researchers are beginning to learn from the data, and what comes next. We discuss the extraordinary scale and diversity of the resource and the growing use of genetics alongside electronic health records and other forms of health data. All of Us is beginning to move from building infrastructure toward producing discoveries that could affect patient care.

Denny explains why the program’s diversity is not simply a matter of representation but a scientific necessity. The project has already identified roughly 1.3 billion genetic variants, including more than 275 million that had not previously been observed. That diversity becomes even more important, Denny argues, as medicine moves toward increasingly personalized predictions and treatments.

“We are capturing such a richer population and so much more kinds of data that we can’t actually reason through it as humans,” he says. “If the data underneath it are highly biased and not representative, then we’re going to make the wrong conclusions.”

We also discuss All of Us as a platform for a much broader picture of human health—from electronic health records and wearables to nutrition, the microbiome, multiomics, environmental exposures, and eventually pediatric data. Denny shares examples of participants whose lives have already been changed by medically actionable genetic results and describes how researchers can build new studies on top of the All of Us population.

What is the next phase of the program and that of precision medicine?

“We’re going to have to redefine our definition of disease,” Denny says. Rather than treating something like type 2 diabetes as a single condition, he imagines increasingly precise descriptions of an individual’s biology, exposures, risk, and response.

After years spent building one of the largest health datasets in the world, All of Us is beginning to show what we might actually do with it.

Welcome to Mendelspod’s 16th season!

It’s hard to believe we’ve been doing this for just over 15 years, following first and foremost the people reshaping biology and medicine. But also the technologies and ideas. Our original vision was to feature the “people behind the headlines” and you’ll see that what we continue to do in this first show of the new season. A sincere thank you to everyone who has listened, watched, subscribed, sponsored, recommended guests, shared a show, told your friends, and simply kept coming back. We have a terrific season ahead, with some of the biggest names in the life sciences as well as some you may not know yet who are asking entirely new questions.

To begin, we wanted to go big picture.

Rob Phillips, professor of biophysics, biology, and physics at Caltech and co-author of Physical Biology of the Cell, thinks this could be the “Newtonian moment” for biology. We have extraordinary amounts of data. What biology needs now, he argues, are concepts that can organize those observations into a more predictive understanding of life.

“The data in biology is getting to the point where we have no excuse,” Rob says.

The conversation ranges from the limits of molecular reductionism to the meaning of prediction, the role of AI, and why better measurement still matters enormously. Phillips makes the case for explanations at many different scales. Understanding a wildebeest migration through molecules, he says, would be like explaining the Golden Gate Bridge through its iron atoms.

Rob is nothing if not provocative—in the best sense. His style is to pile up ideas and questions one on the other to propel his listeners into that creative space of wonder.

“We need more opinions. We need more authenticity,” Phillips says. “I want to hear how people think about things differently.”

On Mendelspod, we’re always searching for scientists who are looking at biology in new ways. Sometimes that means a new technology. Sometimes it means asking entirely different questions. Erdinc Sezgin is bringing the tools of physics to biology.

Sezgin is a group leader at the Max Planck Institute of Molecular Cell Biology and Genetics, where he heads the Membrane Biophysics Group. His research focuses on one of the most familiar yet least understood structures in biology: the cell membrane.

In this conversation from April, Sezgin explains why the membrane isn’t a smooth sea of lipids, but a dynamic landscape of tiny molecular neighborhoods that constantly assemble, disappear, and reorganize. We discuss how he is using the tools of physics to better understand the membrane’s inner and outer lipid layers, each with distinct electrical properties.

Sezgin also talks about his collaboration with Pixelgen Technologies, where Molecular Pixelation was used to study how changes in membrane charge reshape the cell surface. By knocking out a lipid-regulating complex, Sezgin and his colleagues showed that living cells can adopt surface features that alter immune recognition and may help explain how cancer cells evade destruction.

It’s a reminder that major biological insights often arrive alongside new tools that make previously hidden phenomena measurable. Sezgin’s work is also a broader comment on scientific boundaries. Biology is not separate from physics or chemistry, but an expression of them in living systems.

“Cells don’t have physics, chemistry, biology... It is life,” he says.

One of the biggest technology shifts we saw in biology over the past year came from the young company, Cellanome. Instead of relying only on static single-cell snapshots, the Cellanome platform enables longitudinal observation of live cells.

In this conversation, Pier Federico Gherardini, VP of Computational Biology at Cellanome, joins Matthew Spitzer of UCSF, whose lab is putting the Cellanome platform to work in cancer immunology.

The new CellCage technology allows researchers to follow individual cells and their interactions over time, then pair that behavior with transcriptomic and other molecular readouts. As Gherardini explains, this creates “a new data type” that connects functional behavior directly to molecular biology.

For Spitzer, the breakthrough is linking phenotype and function in the same individual cell. His lab can watch dendritic cells activate T cells, or T cells interact with tumor cells, and then ask what was molecularly different about the cells that actually performed the function.

“Now we have measured the function of the cell and the phenotype for the same exact individual cell,” Spitzer says.

The result is a new way to study cell biology that could have implications for cancer immunology, cell therapy, target discovery, and functional screening.

Acute leukemia patients often wait days or even weeks for the full battery of molecular tests needed to guide treatment decisions. Dr. Parth Shah from Dartmouth believes long read sequencing can dramatically shorten that timeline. In this episode, Shah joins Agilent's Rita Shaknovich and Oxford Nanopore's Claire Attwooll to discuss some details of how long reads are beginning to move from research applications into routine clinical testing.

Along the way, we explore the role of targeted enrichment, quality control, automation, and informatics in making these workflows practical for real-world laboratories.

For Shah, the field has reached an inflection point. After more than a decade of development, he argues that long reads are finally positioned to make the leap into clinical genomics. “As we ask more complex questions of human biology, long read is probably going to be the best ammunition that we have,” he says. His team at Dartmouth has already demonstrated the potential in acute myeloid leukemia, where a long-read workflow can now generate a comprehensive molecular profile within 24 hours rather than the weeks often required by conventional testing.

Shaknovich emphasizes that the opportunity is not simply generating more data, but generating better data. Long reads, she notes, can simultaneously capture mutations, structural variants, and epigenetic information, creating a richer biological picture than many existing approaches.

Attwooll highlights the flexibility that has emerged in the long-read ecosystem. Researchers can now choose among whole-genome sequencing, targeted enrichment, and Oxford Nanopore’s adaptive sampling approaches depending on the clinical question. She argues that the field is moving from a niche technology toward a mainstream platform for translational and clinical applications.

A recurring theme throughout the conversation is that no single technology will dominate every application. Whole-genome long reads, targeted enrichment, and adaptive approaches each have a role to play. As these methods move from research into routine testing, success will depend on more than sequencing alone. Agilent's established customer base, automation capabilities, quality-control tools, and experience supporting laboratories help provide the infrastructure needed to bring Oxford Nanopore's rapidly advancing long-read technology into practical clinical workflows.

We’ve become remarkably good at reading cancer cells. Spatial biology enabled us to read them in context. Today we discuss a new Nature study suggesting that the tumor microenvironment—the immune cells, stromal cells, and surrounding biology that often determines whether a therapy succeeds or fails—can be measured from a simple blood draw, or liquid biopsy.

To do that we’re joined by Dr. Vincent Miller, an oncologist and former founding Chief Medical Officer of Foundation Medicine, and Dr. Mirna Jarosz, CEO of LiquidCell Dx.

The work introduces a striking idea. Rather than focusing only on mutations inside tumor cells, it identifies recurring spatial ecosystems within tumors and then shows that their signatures can be recovered from plasma cell-free DNA using methylation patterns. The implication is that liquid biopsy may soon reveal not only what mutations a tumor carries, but how its surrounding biology is organized before treatment ever begins.

But wait. How can blood possibly contain information about spatial organization inside a tumor? That answer unfolds gradually on today’s show, making the final portion of the discussion particularly rewarding.

As Jarosz explains, “We’ve condensed spatial biology to really critical and recurring biological programs. And then we can measure those in blood. So now we have that spatial insight of the tumor microenvironment in a liquid biopsy.”

For Miller, the significance is ultimately clinical. “The tumor is almost like an organ,” he says. “The ability to understand how that organ is constructed and what structures are near one another and how they’re functioning... is really the underpinning” of why patients with seemingly similar cancers can have dramatically different responses to therapy.

If this approach continues to hold up in larger clinical studies, liquid biopsy may expand from reading the genetics of cancer to reading its ecosystem. This shift could improve immunotherapy selection, longitudinal monitoring, and our understanding of cancer biology itself.

For the past decade, liquid biopsy has largely been defined by oncology. Tumors shed DNA carrying mutations and epigenetic changes which allows researchers to detect cancer and monitor response. With this physicians are increasingly able to guide treatment. But chronic diseases are different. There is no tumor. Biological signals are subtle and quite d…

The future of genomics has arrived in Abu Dhabi.

On today’s show, Dr. Mohamed Alameri of the UAE Department of Health and Albarah El-Khani of M42 describe one of the most ambitious precision medicine efforts underway anywhere in the world: the Emirati Genome Program, which has already sequenced more than 900,000 genomes and is rapidly integrating that data into everyday healthcare.

The UAE program is not only a large sequencing effort and database—soon to be made available for research anywhere—but a coordinated national strategy built on prevention, diagnosis, and long-term population health. Particularly striking is the UAE’s focus on inherited and autosomal recessive diseases, which occur at significantly higher prevalence in the region than in many Western populations. Rather than treating genomics as an isolated research exercise, the program has pushed aggressively into premarital screening, newborn genomic screening, pharmacogenomics, hereditary cancer risk assessment, and rare disease diagnosis.

“We truly believe in the philosophy of ‘sequence once, analyze for life,’” says El-Khani. “Imagine a society where every individual from birth holds a whole genome sequence throughout their life. How powerful is that tool at every intersection of public health, clinical care, and screening?”

The scale of the project is already yielding discoveries difficult to achieve elsewhere. According to Alameri, roughly 12% of the variants identified in the Emirati population are not represented in existing global databases, underscoring just how underrepresented Middle Eastern populations remain in genomics research. In some cases, variants previously considered pathogenic in European populations appear to behave differently in Emirati patients, opening entirely new biological questions.

Perhaps the most impressive aspect of the program is the degree to which genomics has been operationalized across the healthcare system. The UAE has invested heavily in physician education and public engagement to move genomics from bench to bedside. Our guests describe a healthcare ecosystem where genomic reports, pharmacogenomic guidance, and hereditary risk assessments are increasingly available directly within clinical workflows.

“The vision was not sequencing everyone for its own sake,” says Dr. Alameri. “It was to build a national asset that could support more predictive, preventative, personalized healthcare for our population and for future generations.”

There is always hype in genomics, as with other emerging technologies. But the UAE effort is already very comprehensive and clinically grounded. This is genomics functioning as healthcare infrastructure in real time.

On today’s show, Dr. Ryan Flynn of Harvard Medical School and Boston Children’s Hospital takes us into a newly emerging layer of biology: the architecture of the cell surface itself. Flynn first gained attention for the discovery of glycoRNA — RNA molecules displayed on the outside of cells — a finding that challenged the traditional picture of the cell surface as a world composed primarily of proteins and glycans. RNA has long been understood mainly as a carrier of genetic information (messenger RNA), but Flynn’s work has show that it has other functions critical to basic processes in the cell.

As we’ve been hearing on the program, biology has largely been a science of inventory. Throughout today’s conversation, Flynn argues that molecular organization itself may be a fundamental biological variable. Not simply whether a molecule exists, but where it exists, what it is adjacent to. Using technologies such as Pixelgen’s Proximity Network Assay, his lab is beginning to map the “cell surface architecture,” or the arrangement of proteins, glycans, and nucleic acids that together govern signaling and cellular behavior.

The implications stretch across biology. Flynn describes early evidence that extracellular RNA can tune classical signaling pathways such as VEGF-mediated angiogenesis by physically modulating how growth factors engage receptors on endothelial cells. Remove the RNA, and growth factor binding changes dramatically. Rather than acting as a simple on/off switch, the RNA appears to function as a finely tuned regulatory layer controlling signaling strength.

In cancer, where cell-surface signaling drives growth, invasion, and immune escape, looking at the organization of the cell surface may determine whether therapies can physically access their targets. Flynn points to bispecific antibodies and T-cell engagers as examples of drugs whose function already depends on proximity and molecular arrangement, even if work in biology has not fully measured those variables before.

For decades, biology has been driven by the powerful notion that if we could sequence enough genomes, transcriptomes, epigenomes, then we could finally explain the cell. On today’s show, Gary Schroth, the Chief Scientific Officer at Cellanome, argues that something essential was still missing.

Schroth spent nearly two decades at Illumina helping build the sequencing revolution. He has now joined Cellanome to pursue an expanded vision of biology that connects transcriptomics with live-cell imaging. Our conversation centers around two newly released preprints describing the company’s platform and its application to CRISPR screening, where imaging and transcriptomic data are explicitly linked in the very same cells.

“What we show in a few examples in both papers,” Schroth explains, “is that it’s the combination of transcriptome information and imaging information that really gives us the complete story of what that cell is doing.”

That idea—linking what researchers literally see under the microscope with the molecular state of the exact same cell—emerges as the core concept of the interview. Rather than treating imaging and transcriptomics as separate measurements, Cellanome brings them together in a longitudinal workflow where cells can be observed alive over time and then profiled at the transcriptomic level.

“Sequencing has certainly taught us a lot about cells and sort of the parts list inside cells,” he says. “But it doesn’t really explain biology.”

Will this be the next phase of post-genomic biology where the field moves beyond static snapshots toward directly observing cellular function as it unfolds?

Today on the show, we’re discussing a new study just presented at ASCO 2026 that could change how chemotherapy decisions are made for a large group of breast cancer patients.

During ASCO we spoke with Phil Febbo, Chief Scientific and Medical Officer at Veracyte, and John Leite, the company’s Chief Commercial Officer, looking at the results from the OPTIMA study, a large prospective trial involving roughly 4,500 patients with clinically high-risk ER-positive, HER2-negative breast cancer. The study found that about two-thirds of these patients could safely avoid chemotherapy when treatment decisions were guided by the Prosigna test.

“What the Optima study shows definitively is that those women with low Prosigna score do not benefit from chemotherapy,” Febbo explains. “They get all the side effects… without any benefit.”

The data generated favorable attention at ASCO. The study produced prospective level 1A evidence, the highest standard for predictive testing, and addressed one of the central problems in breast cancer care: determining which patients actually benefit from chemotherapy and which patients may be exposed to toxic treatment unnecessarily.

Our show also looks at the broader evolution of molecular diagnostics in oncology. Prosigna runs on whole transcriptome sequencing, creating opportunities not only for current clinical decision-making, but also for future translational research into tumor biology and treatment response.

“We need the full complement of the transcriptome to understand what is the faulty circuitry and how do we shut it off therapeutically,” Leite says.

Veracyte has moved quickly from clinical validation to rollout. The company already has the assay prepared for U.S. launch immediately following the ASCO presentation. If only it worked out this way every time. It’s the kind of direct through-line between biology, clinical evidence, and improvement of human life that molecular diagnostics companies strive for each year.

Note: For more in-depth discussion on the OPTIMA study and the launch of Prosigna, sign up for an upcoming webinar at GenomeWeb here.

For years, precision oncology has largely been discussed through the lens of breakthrough drugs. But there’s another story running underneath modern cancer care: the quiet rise of companion diagnostics. These tests are increasingly deciding who receives those therapies in the first place. In many cases, the real bottleneck is no longer discovering a drug target. It’s building a reliable system for identifying the right patient at the right moment in the disease. That challenge sits at the center of this conversation with Rita Shaknovich, Chief Medical Officer for Life Sciences and Diagnostis, and Karina Kulangara, Associate Vice President of R&D in Companion Diagnostics at Agilent Technologies.

Agilent has always had a major role in this field. Rita and Karina explain how companion diagnostics evolved from the original Herceptin test into a vision for a much broader ecosystem spanning pathology, automation, regulation, and global clinical deployment.

We dive into Agilent’s recent FDA approval expanding PD-L1 IHC 22C3 PharmDx into ovarian cancer, a development both guests describe as particularly meaningful given the historically poor outcomes associated with the disease. As Rita puts it: “Precision medicine is based fundamentally on scientific truth . . . it brought real results for patients. It brings better survival for patients, fewer side effects from the medication.”

Karina offers one of the clearest explanations we’ve heard for why immunohistochemistry or IHC has endured so long in modern oncology. “It’s the ability to detect protein biomarker in the spatial context of the tissue,” she explains, emphasizing that location and cellular context can fundamentally shape how therapies work.

What emerges is a picture of precision oncology that is becoming less exotic and more routine. We’re talking not just new drugs, but an entire clinical and technological infrastructure which is designed to match therapies to biology more effectively and over time.

Dr. Eric Green returns to Mendelspod in a new role: Chief Medical Officer of Illumina. After more than three decades at the National Human Genome Research Institute, where he helped guide genomics from research initiatives to clinical reality, he now joins one of the industry’s most influential companies at a moment when the field is expanding beyond DNA alone.

Green takes us on a tour around the world of multi-omics, which he says is not a branding exercise but a practical response to the limits of sequence data by itself. Genomics remains foundational, but many clinical questions require additional layers of biology, including RNA, epigenomics, proteomics, and single-cell analysis. As he puts it, “DNA sequence alone may not reveal it.”

The discussion highlights rare disease as one of the clearest examples. Genome sequencing can solve roughly half of suspected cases, Green notes, but many patients remain undiagnosed because the relevant signal may lie in RNA splicing, epigenetic regulation, structural variation, or downstream protein effects. In those settings, multi-omic approaches can provide the missing evidence needed to move from uncertainty to diagnosis.

In oncology, the challenge is different. Cancer genomes can be highly complex and heterogeneous, making it difficult to distinguish driver events from background noise. That is one reason why researchers and clinicians are increasingly incorporating methylation markers, transcriptomic data, and proteomic signals into early detection, disease sub typing, and monitoring strategies.

Green also emphasizes that the next bottleneck may be less about generating data than interpreting it. “The human brain is not going to be the thing that’s going to crack this nut,” he says. “It’s going to be AI and computational biology.”

The result is an overall picture of where the field may be headed as we go from genomic medicine to a broader molecular medicine with multiple data types that will improve diagnosis, stratify disease, and guide care worldwide.

For years, proteomics was described as the missing layer of biology. Why missing? Because measuring proteins at scale turned out to be vastly harder than sequencing DNA.

That may finally be changing.

In today’s episode Theral speaks with Dr. Yan Zhang, President of Proteomic Sciences at Thermo Fisher Scientific, about the rapid evolution of large-scale p…

We’ve gotten very good at reading DNA. We’re just beginning to understand how to read its state.

On today’s show, Rob Osborne, Senior Vice President of R&D at Biomodal, discusses new evidence that separating two epigenetic marks—5-methylcytosine and 5-hydroxymethylcytosine—can improve early cancer detection from liquid biopsy. In a recent Nature Communications Medicine study, his team showed that analyzing these signals independently in circulating DNA significantly enhanced detection of Stage I colorectal cancer compared with approaches that combine them.

The advance does not require new sequencing hardware. Biomodal’s approach uses a sample preparation kit compatible with existing platforms, paired with bioinformatics tools, potentially lowering the barrier to adoption while expanding the information content of standard sequencing workflows.

The underlying insight is biological as much as technical. Most methylation assays collapse 5mC and 5hmC into a single signal, masking early transitions in gene regulation. Osborne describes this as “squishing them into one output,” a simplification that can obscure meaningful changes in disease onset and progression.

By separating the signals, the study identified patterns that emerge earlier in tumor development, offering a more sensitive window into disease biology.

But the deeper message of the interview is that this work may only scratch the surface. “I think that we’re just at the beginnings of really understanding this biology,” Osborne says.

On today’s show, a fascinating discussion about digital twins for medical research— leading to the 64 million dollar question of how much of the current AI wave in healthcare may eventually prove real.

Drawing on her background in cancer research and now as CEO of Concr, Dr. Irina Babina joins us to argue that the future of oncology may depend less on g…

An inventor of Solexa sequencing by synthesis has a new idea.

On today’s show, Sir Shankar Balasubramanian revisits the accidental origins of Solexa sequencing, born not from a sequencing project at all, but from curiosity-driven experiments watching DNA polymerase at work. What followed helped transform DNA sequencing from a specialized pursuit into a routine engine of modern biology. But as Shankar makes clear, the biggest surprise may not have been genomics itself—it was how next-generation sequencing became a universal readout for biology, powering everything from single-cell and spatial biology to entirely new ways of probing molecules and mechanisms.

Our conversation then turns to his latest venture, Biomodal, and the emerging world of 6-base sequencing. Shankar explains why distinguishing 5mC and 5hmC matters, and how six-base sequencing may improve early cancer detection. 6-base sequencing could also aid researchers in the exciting frontier of neurobiology.

As always with great scientists, the story widens beyond any single technology. Shankar closes by reminding us that discovery follows better measurement. As our tools improve, biology will continue to surprise us.

“That is what research is. It’s stepping into the unknown,” he says.

There’s a famous line attributed to Ernest Rutherford, the father of nuclear physics: “All science is either physics or stamp collecting.” It’s still provocative. But it’s unfair to biology. Long before today’s omics era, biologists were uncovering causality everywhere from evolution and natural selection to Mendelian inheritance. They have never merely catalogued life. They have explained it. But modern biology has also generated extraordinary inventories of genes, proteins, and pathways, and those inventories now invite a deeper systems-level question: how do the parts behave together in living cells? Could new precise physical measurements aid biology and medicine?

Todays’ guest, Erdinc Sezgin, is an Associate Professor at Karolinska Institute and recipient of the Biophysical Society Early Independent Career Award. His lab is bringing physics to biology. For example, Sezgin studies the cell membrane not as a passive wrapper, but as an active, dynamic system whose physical properties of fluidity, viscosity, charge, and organization help determine how cells signal and survive. His hope is to improve ways to measure these biophysical properties.

Sezgin discusses his recent collaboration with Pixelgen Technologies, where Molecular Pixelation was used to study how changing membrane charge reshapes the cell surface. By knocking out a lipid-regulating complex, Sezgin and his colleagues showed that living cells can adopt surface features that alter immune recognition and may help explain how cancer cells evade destruction. It’s a reminder that major biological insights often arrive hand-in-hand with new tools that make previously hidden phenomena measurable.

The conversation closes on a broader point about scientific boundaries. Biology is not separate from physics or chemistry, but an expression of them in living systems.

“Cells don’t have physics, chemistry, biology. . . It is life,” he says.

You’ve heard of 5-base genomics. How about 6-base? It turns out that separating 5-methylcytosine (mC) and 5-hydroxymethylcytosine (hmC) is pretty important.

Peter Fromen has had a front-row seat to the evolution of sequencing, from the rise of high-throughput genomics at Illumina to long-read technologies at PacBio. Now, as CEO of Biomodal, he’s focused on integrating genetics and epigenetics into a single workflow—and showing that the regulatory layer of the genome may be where the next breakthroughs lie.

Chapters:

0:00: Why epigenetics needed a reset12:07 The colorectal cancer study and early detection signal16:41 Building the 6-base ecosystem21:23 Commercial traction and the road to the clinic

In today’s program, Fromen explains why distinguishing between mC and hmC changes how we read biology. Biomodal’s recent colorectal cancer study begins to demonstrate that value in practice.

“We ultimately ended up generating an AUC of 95%,” he says, describing early-stage detection results that point to the power of combining both signals.

More broadly, he frames hydroxymethylation as an early indicator of disease.

“hmC is essentially the canary in the coal mine for early disease detection.”

We also discuss the practical side—what a 6-base workflow looks like in the lab and where the company sits commercially as it pushes toward clinical validation.

Will this be the new standard for how we read biology?

What is the value of someone’s genome over their life? Is a genome today what it was 10 years ago? How does the adoption of genomic testing compare to other areas in medicine, such as imaging or electronic health records?

Today we take a pretty comprehensive look at genomic testing in practice with Damon Hostin, Head of Market Access, Clinical Solutions at Illumina. Damon brings a rare perspective to this conversation. He’s been in the field since the Celera era, when sequencing was helping define modern genomics, and he’s also worked on the front lines in a large community health system, CommonSpirit Health. At Illumina, he speaks regularly with payers and other stakeholders.

Across oncology, rare disease, reproductive health, and pharmacogenomics, Damon describes a field that has clearly moved into standard of care in key areas—but is still very much in the phase of identifying the “eligible but under-tested.” Adoption is real, but it’s incomplete.

Chapters:

0:00 Genomic medicine arrives4:51 Genomics, imaging, and the EMR11:23 Oncology—from diagnostics to decision-making18:16 Rare disease and reproductive genetics28:51 The lifetime value of a genome36:03 Cost, quality, and what a genome is

A central idea running through the podcast is that the genome is no longer a one-time diagnostic. Its value compounds over time as databases grow, variants are reinterpreted, and new therapies emerge.

At the same time, even the basic notion of what a “genome” is, is beginning to shift. With the rise of multi-omic data—transcriptomics, proteomics, methylation—the question is no longer just cost per genome, but what kind of biological insight we’re actually measuring. “A genome isn’t a genome isn’t a genome,” Damon says.

He ends with a line that neatly reframes the entire debate around cost: “When you look at the cost of healthcare . . . the cost of the genomics is almost nothing.”

Genomic medicine is here. We’re now wrestling with how to scale it, how to use it earlier, and how to make it part of the everyday infrastructure of care.

Note: For more discussion and analysis on this topic, check out this upcoming Virtual Roundtable Discussion at GenomeWeb.

Fei Chen of the Broad Institute describes the original problem simply: genomics gave us powerful inventories of gene expression, while microscopy gave us structure—yet the two lived in separate worlds. “You could either have your structure or you could have gene expression, but you couldn’t have both.”

In this conversation, Fei walks us through how Slide-tags—now commercialized as Takara Bio Trekker technology—set out to close that gap. Instead of mapping gene expression onto a grid, his team flipped the problem: barcoding the cells in place, then reading them out with single-cell sequencing. The result is something closer to a GPS system for cells.

What this unlocks is not just better maps, but better biology. Better questions. In cancer, Fei describes the discovery of local immune “circuits” that determine whether tumors respond to immunotherapy. And more broadly, spatial data turns tissue itself into a kind of experiment itself. Is this the biology of the future? “The spatial context is a natural experiment that has happened.”

Chapters:

0:00 The problem: structure vs gene expression1:36 A GPS for cells8:59 Immune circuits and cancer response20:04 Tissue as experiment26:24 New questions for biology

Across applications, Fei emphasizes that the real shift is conceptual. Spatial biology is not just about adding location to sequencing. It’s about learning how to ask new questions—ones that treat cells not as isolated units, but as participants in research.

Peptides are having a moment.

But beneath the market excitement and the GLP-1 headlines, something more interesting is going on. A field that for years seemed technically promising but perpetually constrained is becoming wide open.

To see into that open terrain, we’re joined by Charlie Johannes, founder of EPOC Scientific and president of the Peptide Drug Hunting Consortium, along with Tomi Sawyer, a founder of the Consortium and founder of Maestro Therapeutics. We asked them for a high-level look at a field being reshaped by advances in chemistry, screening, delivery, and by a growing sense that peptides may be uniquely positioned to open up biology that other modalities have only partly been able to reach.

And yet both are clear: the field is not mature. AI is accelerating biology, which still depends on existing knowledge. Prediction remains limited, especially with non-natural chemistry. And the core challenge may now be human—how to turn an overwhelming amount of data into real innovation. As Johannes puts it, “Turning knowledge into innovation is the real challenge.”

Chapters:

1:31 Why peptides are suddenly hot again6:10 Between small molecules and biologics10:14 Oral delivery, screening15:45 AI, automation, and the limits of prediction32:17 The Consortium and where the field is heading

This is not a finished revolution—it’s a launch. The field, Sawyer says, is “in the Artemis II rocket right now heading towards the moon.” The peptide story is now much bigger than obesity drugs. Where does the field stand today? What has changed, and what remains difficult?

This episode is the first in a new partnership between Mendelspod and Peptide and Protein News, a media platform covering peptide and protein drug development. You can see what they’re up to at peptideandprotein.com.

It was the biggest story in sequencing last year: Mark Kokoris, head of SBX sequencing at Roche and inventor of the technology, joins Mendelspod to talk about how Sequencing by Expansion (SBX) works and why it may redefine the limits of genomics.

* 0:00 A long journey inspired by PCR

* 7:20 What is sequencing by expansion?

* 14:00 On scale and accuracy

* 19:40 Multi-omics vision?

* 24:40 What will be the killer app?

* 30:00 Biggest challenge for launch

Kokoris recounts the long path from co-founding Stratos Genomics in 2007 to Roche’s acquisition in 2020, when his team’s “wildly ambitious chemistry” finally found its match in Genia’s high-density nanopore platform. “Our approach to efficiently sequencing DNA,” he explains, “is to not sequence DNA. We rescale the problem—expand the molecule about 50-fold—so we can read it with much higher signal-to-noise.”

The result is astonishing speed. Working with the Broad Institute and Boston Children’s Hospital, SBX delivered whole-genome results in under four hours, with the sequencing step itself taking only about 15 minutes. Kokoris attributes the achievement to a confluence of chemistry and compute.

SBX’s duplex mode achieves Illumina-level accuracy (F1 > 99.8 %) while maintaining single-molecule simplicity. Its tunable flexibility lets small labs run a handful of samples in hours or large centers run thousands per day. Kokoris describes it as a technology built on impatience and rule-breaking, designed to give scientists options they’ve never had.

Looking ahead to the 2026 research-use launch, he’s characteristically bold:

“For me, success means SBX becoming the new standard in sequencing. Innovation can’t stop—it has to keep evolving, because biology is complex and we’ve got a lot more to do.”

This show was originally published Nov 11, 2025.

Why do some animals live ten times longer than others?

That question opens today’s interview with Steve Austad, Distinguished Professor at the University of Alabama at Birmingham and one of the leading thinkers in the biology of aging. It quickly becomes clear why he’s been such an important voice in bringing aging research from the margins into the center of science. As he puts it, the field was once “where scientists went to die,” but with modern genetic and molecular tools, it has become one of the most active areas in biomedicine.

Steve’s approach, laid out in his book for the empiricist (I’m an amateur), Methuselah’s Zoo, is deceptively simple: look at the animals. From birds and bats to clams that live for centuries, he shows that lifespan follows a clear evolutionary logic. Safer, more stable environments favor slower aging. “If it’s unstable and unsafe… it makes sense… to reproduce fast,” he explains, while protected environments allow organisms to invest in long-term maintenance. It’s a framework that turns curiosity into theory—and theory into something testable.

Chapters:

1:31 Where scientists went to die4:11 The opossum problem8:00 Air, land, sea14:23 The longevity quotient33:30 Not forever, just longer

What makes Steve such a compelling guide is his tone. He’s low-key, almost amused at times, but unwavering on the science. Aging, he reminds us, isn’t programmed for our benefit—“evolution does not care how long you live.” That doesn’t mean we can’t intervene. The field is now moving into human trials, even if key tools like aging clocks are still imperfect. He has little patience for talk of immortality—calling it “completely delusional.” Still, he’s optimistic. Adding a decade or two of healthy life—not forever—is the goal today.

Molecular residual disease, or MRD, has been part of oncology’s vocabulary for decades. But knowing something is there and being able to measure it precisely are two very different things. In today’s show, we explore how MRD testing moved from a long-standing clinical suspicion to one of the most consequential tools in modern oncology.

Joining us on the program are Chris Hourigan, Director of the Fralin Biomedical Research Institute Cancer Research Center (DC) at Virginia Tech, bringing the academic and clinical AML lens, and Gary Pestano, Chief Scientific Officer at Biodesix, offering the industry and diagnostic development perspective.

Hourigan reminds us that MRD itself isn’t new. What was missing were the tools. From counting cells under a microscope to flow cytometry and now highly sensitive molecular techniques including droplet digital PCR, MRD has evolved into a quantitative, actionable signal.

Coming from the side of commercializing and scaling assays, Pestano underscores the central challenge of distinguishing meaningful signal from background noise. “There is a lot circulating in our blood. The key is what is meaningful, what is not meaningful,” he explains. Sensitivity alone isn’t enough. Target selection, bioinformatic filtering, validation at scale, and real-world reproducibility all determine whether MRD can truly guide care.

The field is very much still work in progress, say both.

Looking ahead, they point toward quantification as the next frontier. MRD is no longer just about detecting what remains. It’s about deciding what happens next.

“We’ve been talking about MRD as if it’s a binary concept” says Hourigan. “I can imagine in the future, there’s going to be windows, and we will tune therapy to what comes next.”

Thank you to Bio-Rad for sponsoring today’s show. Bio-Rad is your trusted partner for absolute quantification and reproducible results in oncology research. Bio-Rad helps you move from data to confident decisions. Learn more at bio-rad.com/oncology.

For two decades, tests like Oncotype DX have helped oncologists decide which early-stage breast cancer patients should receive chemotherapy. But those tools were designed mainly to predict early recurrence, leaving physicians with far less clarity about the risk that cancer might return years later.

For today’s program, George Sledge, Chief Medical Officer at Caris Life Sciences, discusses new findings from the TAILORx trial showing how multimodal AI—combining molecular sequencing, digital pathology, and clinical data—can improve long-term prediction of breast cancer recurrence.

Sledge explains that breast cancer recurrence may actually reflect two different biological processes unfolding over time. Molecular signals captured through RNA analysis appear most informative for predicting recurrence in the first five years, while computational analysis of digital pathology images becomes especially powerful for predicting recurrence later in the disease course.

“The best results come from looking at multiple omic levels,” Sledge says, describing a shift away from single biomarker tests toward integrated biological analysis.

We began this podcast back around the time the ENCODE project announced that much of the genome was biochemically active. The big science project was undoing the tidy idea of “junk DNA,” and not without controversy. But activity is not the same as purpose. On today’s show, we move past the question of whether the non-coding genome does something and ask a more ambitious one: why has evolution retained so much genomic material unless it carries adaptive potential?

Theral speaks with Sudhakaran Prabakaran, computational biologist at Northeastern University and founder of NonExomics, about his provocative new book, “Eclipsed Horizons: Unveiling the Dark Genome.” Drawing on his lab’s work cataloging more than 250,000 non-canonical proteins, Prabakaran argues that regions outside traditional gene definitions are constantly generating novel open reading frames—previously unrecognized proteins that may shape adaptation, speciation, and disease.

Chapters:

(00:00) Identical Genomes, Wildly Different Fish

(04:00) The Dark Proteome Wakes Up

(10:00) Protein Pop-Up Shops

(20:00) Homo Minimus and the Space Thought Experiment

(30:00) Precision Medicine Beyond the Exome

From rapidly diversifying cichlid fishes to human accelerated regions (HARs) of the human genome linked to schizophrenia, he makes the case that protein birth and death is continuous, cheap, and exploratory. In his framing, the “dark genome” functions less like debris and more like a flexible evolutionary sandbox—capable of producing latent biological parts that can be deployed under stress or even extreme environments like spaceflight.

The book goes beyond ENCODE’s demonstration of activity and asks what that activity is for, crossing into that taboo in biology, teleonomic analysis. Weaving together proteomics, evolutionary biology, information theory, and even speculative extensions into space biology, Prabakaran suggests that genomes may be structured not just to preserve past adaptations, but to enable future ones.

For those of you staying put on the ground, the implications are very tangible for precision medicine. His company NonExomics is using non-canonical protein signatures to stratify cancer patients and refine difficult diagnoses, arguing that the next wave of biomarkers may lie outside the exome.

Provocative? Certainly. Grounded in emerging proteomics tools and real clinical cases? Also yes. This conversation probes directly into that mysterious future of biology.

“We have been talking now for 15, 20 years about the diagnostic odyssey. That shouldn’t exist anymore. The new odyssey is the therapeutic odyssey.”

That’s Stephen Kingsmore, president and CEO of Rady Children’s Hospital (he just announced his retirement), explaining the impact of a new genome mapping technology from Illumina.

Whole-genome sequencing has transformed diagnosis, but some of the hardest pediatric cases persist because the structure of the genome has remained difficult to resolve. Today on Mendelspod, we cover Illumina’s newly launched proximity mapped reads, showing how long-range genomic context can be captured directly on existing Illumina sequencers and integrated into the lab workflow. The conversation traces how this added structural clarity is already improving diagnostic confidence and, critically, enabling highly precise n-of-1 therapies such as antisense oligonucleotides (ASOs).

Olivia Kim-MacManus, a pediatric neurologist and ASO trial leader, shows how the new diagnostic precision directly feeds therapeutic design.

“All of these genetic therapy approaches hinge on precise diagnostics,” she notes, emphasizing that allele-specific and haplotype-aware targeting is essential for ASOs and other emerging gene-based interventions.

From the product and workflow side, Ali Crawford joins us as Senior Director of Science Research at Illumina, detailing how the technology works without requiring new instruments or complex workflows, eliminating the need for separate library preparation steps.

“You just order the kit and go,” she says, highlighting how preserving spatial information on the flow cell unlocks variant calls and structural insight that were previously inaccessible with their standard short-read sequencing.

When genome structure comes into better focus, treatments are no longer theoretical.

CareDx is a company on the move. For years, they have been a bellwether in molecular diagnostics. Their early bet on gene expression testing in transplant medicine, their bruising fight over Medicare coverage, and their pivot into cell-free DNA monitoring have all reflected the growing pains of precision medicine itself.

Now, under CEO John Hanna, the company looks less like a single-test diagnostics firm and more like a clinical ecosystem.

Hanna brings an unusual vantage point. He began his career in health insurance before moving into molecular diagnostics—giving him insight into both innovation and reimbursement. That dual perspective shaped CareDx’s recent evolution: focus tightly on a defined clinical niche—transplantation—while expanding horizontally into the tools, software, and services that surround it.

Today, CareDx operates across three segments: lab products (including high-resolution HLA typing kits using PCR, NGS, and nanopore), a growing software and patient solutions business, and its flagship genomics portfolio led by AlloSure, its donor-derived cell-free DNA assay. What distinguishes the company now is its “solution selling” approach—engaging transplant centers not just with a test, but with workflow software, quality reporting tools, specialty pharmacy, and EMR integration.

“Our solution selling strategy is working,” he says today.

At the scientific core remains the effort to replace invasive biopsies with molecular monitoring. AlloSure’s innovation—detecting donor-derived cell-free DNA without requiring donor genotyping—made routine blood-based rejection monitoring scalable. Yet adoption is not purely technical.

“The biggest challenge with our space is building belief that molecular testing can replace tissue biopsy.”

Clinician education, clinical trials, and guideline inclusion remain central to shifting standards of care. CareDx has leaned heavily into this, hiring medical leadership specifically to translate data into practice. The company is also layering AI on top of its molecular assays. AlloSure Plus integrates genomic results with EMR-derived clinical variables to generate a rejection risk score. CareDx’s operational mantra has been to put the burden of complexity on the company, not the clinician.

Large-scale genomics is back — and this time, it’s global by design.

In this episode of Mendelspod, we return to the kind of ambitious, shared genomics project that helped define the field a decade ago. The Global Parkinson’s Genetics Program (GP2) has now genotyped more than 100,000 participants worldwide, with roughly one third of samples coming from historically underrepresented populations. That scale and diversity are already reshaping how Parkinson’s disease is studied — and how it may eventually be treated.

My guests are Andrew Singleton, co-lead of GP2, and Ignacio (Nacho) Mata, a geneticist at Cleveland Clinic and founder of the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD). Together, they describe how globally representative datasets are not a political aspiration, but a scientific necessity — especially in an era of precision medicine.

Singleton explains that studying Parkinson’s across populations doesn’t just broaden participation; it increases scientific power.

“The more we learn about individual populations, the more we understand about disease as a whole — and the more chances we have to come up with treatments for disease as a whole,” he says.

Mata brings a complementary perspective from years of building Parkinson’s genetics infrastructure in Latin America. He emphasizes that without inclusion in genetic and biomarker research, entire populations risk being excluded from the next generation of molecularly targeted therapies.

“If we don’t have our patients studied for genetics or biomarkers, then those patients will not have access to the new treatments,” he notes, adding that GP2 is designed to narrow rather than widen existing health disparities.

We explores how GP2’s open-science structure has been key to its success and could serve as a model for other global research projects. GP2 has invested heavily in training and infrastructure so that researchers around the world can lead analyses locally, rather than simply contributing samples.

As both guests make clear, this is only the beginning. With hundreds of thousands of samples committed and a new generation of globally distributed investigators, GP2 is laying the groundwork for biologically defined subtypes of Parkinson’s and for more precise diagnostics and disease-modifying therapies.

When genomics gets big enough — and inclusive enough — scale itself becomes a discovery.

What if the hardest part of scaling cell therapy turned out to be a materials problem not a biological one—and the solution looked like a sponge?

On today’s show, Theral speaks with Yev Brudno, Associate Professor in the School of Pharmacy and also the Department of Biomedical Engineering at the University of North Carolina at Chapel Hill, about a deceptively simple technology that could dramatically accelerate manufacturing and lower the cost of cell therapies. Brudno’s lab works at the intersection of chemistry, biomaterials, and cell biology, with a focus on removing the manufacturing and scalability barriers that have kept powerful therapies like CAR-T out of reach for most patients.

At the center of the conversation is a dry, porous biomaterial sponge—developed initially by accident—that boosts viral transduction efficiency from roughly 10% to as high as 90% by forcing cells and viral vectors into intense, highly efficient contact. The sponge works across multiple delivery systems, including retroviruses, lentiviruses, AAVs, and even lipid nanoparticles, effectively functioning as a low-cost, scalable alternative to complex microfluidic systems. Brudno explains how this discovery reframes genetic modification as a physical- and materials-science problem rather than a purely biological one.

The discussion goes beyond mechanism into real-world impact. Brudno describes how these sponges—now commercialized for research use by Takara Bio USA—could compress weeks-long CAR-T manufacturing workflows into hours, enabling bedside or community-hospital cell engineering without the need for $100-million cleanroom facilities. The episode closes with a broader reflection on the future of cell therapy.

Once again, some of the most transformative advances might come from curious bench science and happy accidents rather than prediction alone.

For today’s show, we return to discussing the exciting new Cellanome platform. Joining Theral are Pier Federico Gherardini, VP of Computational Biology at Cellanome, and Matthew Spitzer, Associate Professor at University of California, San Francisco, whose lab is using Cellanome’s CellCage technology to study immune cells in dynamic, interactive contexts.

0:00 From static snapshots to observing cell function in real time

4:45 Pairing phenotype with function like we never could before

7:30 Can see cell-cell interaction

19:40 Early applications

Rather than relying on static single-cell snapshots, the Cellanome platform enables longitudinal observation of live cells—tracking division, interaction, and function over time—before pairing those behaviors with transcriptomic and molecular readouts. As Gherardini explains, “This creates essentially a new data type where you observe cells over time… and then you can pair all of that functional information with the molecular readouts that you get from sequencing.”

For Spitzer, that shift fundamentally changes what can be known. Traditional approaches often force scientists to infer function indirectly, correlating phenotype measured in one experiment with behavior measured in another. With CellCage, his lab can finally measure both in the same individual cell.

“Now we have measured the function of the cell and the phenotype for the same exact individual cell,” Spitzer says, “and this allows us to really understand how those core characteristics are linked in a much more detailed way.”

For Spitzer, a major advance comes from observing cell–cell interactions as they unfold. Where previous methods could show proximity in a tissue section, they could not reveal outcomes. Using Cellanome, Spitzer’s team can now watch whether a T cell activated by a dendritic cell actually proliferates, produces effector molecules, or kills a tumor cell—and then trace those outcomes back to specific molecular programs. This has already revealed surprising heterogeneity within supposedly uniform cell populations, identifying rare but highly potent immune cells that would have been invisible in bulk assays.

Looking ahead, both guests see immediate applications in cell therapy development, target discovery, and functional CRISPR screening—areas where measuring what cells actually do matters more than what they merely express. We close with a sense that cell biology is entering a new phase—one where function, interaction, and time are no longer inferred, but directly observed, measured, and modeled.

Half of oncologists in the U.S. are now ordering MRD testing, according to Adam ElNaggar, MD of Natera — but the other half, he says, “are still figuring out how to use it, or that it even exists.”

In this episode, we talk with ElNaggar about the rapid rise of ctDNA-based monitoring and how it’s changing the very rhythm of cancer care. From Natera’s “tumor-informed” SignateraTM assay to its new “tissue-free” LatitudeTM test, the company is reshaping oncology around the molecular traces that cancer leaves behind.

“ctDNA-negative patients have an extremely low likelihood of showing disease on imaging,” he explains. “So rather than scanning every few months, we can tailor follow-up to when it’s actually needed—and spare the anxiety and cost that come with it.”

The conversation also covers Natera’s EmpowerTM hereditary cancer panel, which has expanded testing to all patients with ovarian and endometrial cancer, and a new Hereditary Cancer Alert program that nearly doubled testing rates among eligible patients. ElNaggar describes how hereditary and MRD testing now reinforce one another, helping clinicians catch missed cases and close the loop for families.

We finish with a look ahead: a future where ctDNA status becomes a staging element, where clinical trials are shortened by molecular endpoints, and where multi-omic assays—combining DNA, methylation, and protein—push oncology toward truly personalized medicine.

“We’re reaching the point,” says ElNaggar, “where staging won’t just be about pathology—it’ll be about biology.”

Note about trials mentioned:

IMvigor010 compared adjuvant atezolizumab to observation (surveillance) in an unselected muscle-invasive bladder cancer (MIBC) population

IMvigor011 prospectively randomized only ctDNA-positive MIBC patients to atezolizumab versus placebo

See all SignateraTM Publications here.

Aging may be the last great frontier of precision medicine—not a single disease, but the slow re-patterning of immunity, metabolism, and resilience that determines how well and how long we live.

In this wide-ranging and genuinely mind-bending conversation, Alan Landay and Tom Blackwell make a compelling case that aging itself is finally becoming a legitimate—and testable—target of medicine.

For Landay, the path into aging biology began decades ago through HIV. Antiretroviral therapy transformed HIV from a fatal disease into a chronic one—but something didn’t add up. Patients were surviving, yet developing cardiovascular disease, neurocognitive decline, and metabolic disorders years earlier than expected. The immune system recovered on paper, but inflammation never fully resolved. That realization led Landay to view HIV as a model of accelerated aging, and to ask whether the same inflammatory processes drive aging in the broader population.

As he explains, “we realized that persistent inflammation was the driver—pushing comorbidities forward in time. That’s when HIV stopped being just an infectious disease and became a window into aging itself.”

Over the past decade, Landay has brought the full toolkit of systems biology to that question—proteomics, metabolomics, glycomics, microbiome analysis, and epigenetic clocks—to understand why some bodies grow frail while others remain resilient. A central theme is the gut: age-related changes in the microbiome weaken the intestinal barrier, allowing inflammatory signals to leak into circulation and quietly accelerate biological aging.

Blackwell approaches the same problem from the clinic. As a geriatrician, he sees that most people ultimately die from one of three conditions—heart disease, cancer, or dementia—and that aging is the common denominator behind them all. His bold question is not whether we can treat these diseases individually, but whether we can slow the biological aging process that gives rise to them. That question underpins his ongoing clinical trial testing tirzepatide, a GLP-1–based therapy, not for weight loss, but for its potential to slow aging itself. “

“There is no drug in the world proven to slow aging,” Blackwell says. “We haven’t proven this one either—but we’re finally running the experiment that can give us a real answer.”

At the heart of the discussion is a shared fascination—and healthy skepticism—around aging clocks and biomarkers. Both researchers are using advanced epigenetic and proteomic clocks, including the DunedinPACE measure, to track whether interventions truly change the rate at which people age biologically. The clocks are powerful, but not yet definitive.

The episode also explores how geroscience has moved from the fringe to the mainstream: NIH-wide initiatives, ARPA-H funding, repurposed drugs, and growing FDA openness to aging as a trial framework. Rather than chasing immortality, both guests emphasize healthspan—more years of mobility, cognition, and social engagement.

“Our vision isn’t to live longer in a nursing home. It’s having a lot more 98-year-olds who drive themselves to clinic, go on dates, and still love their lives,” says Blackwell.

The RNA revolution didn’t end with COVID. It’s only just beginning.

Today Theral is joined by Andrew Geall, co-founder and Chief Development Officer of Replicate Bioscience, to explore why self-replicating RNA may represent the next major leap in vaccines and therapeutics. While first-generation mRNA proved what was possible in a pandemic, Andrew argues …