Mendelspod Podcast

Theral Timpson

Offering a front row seat to the Century of Biology, veteran podcast host Theral Timpson interviews the who's who in genomics and genomic medicine.

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36 episodes
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Thursday · 18 min
How Roswell Park Is Rethinking Molecular Testing for Blood Cancers with Sean Glenn
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
Wednesday · 25 min
Building a Single Cell Atlas of the Poplar Tree with Robin Buell and Anita Giabardo
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
Tuesday · 28 min
Three Illumina 5-Base Grant Contest Winners Share Cancer Research Insights from Combining Epigenetics and Genetics
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
October 8 · 5 min
Ten Years Ago Mike Joyner Was Skeptical of Precision Medicine. What Does He Say Today?
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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?
September 29 · 5 min
Deeper into the Genome: Google DeepMind’s Ziga Avsec on the New AlphaGenome Atlas
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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/
September 22 · 22 min
Mass Spec Comes to the Routine Core Lab: Don Mason of Roche on a New Era in Clinical Testing
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
September 17 · 5 min
Jennifer Dionne Wants to Read the Whole Proteome Using Nanophotonics, i.e. Raman Spectroscopy
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.
September 15 · 6 min
Katie Maloney, Partner at DeciBio Consulting, Sees an Inflection Point for Digital and Computational Pathology
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.”
September 11 · 5 min
It’s One of the Greatest Success Stories of Molecular Medicine. Genomics Historian Kevin Davies on His Latest Book about Casgevy and Sickle Cell Disease
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.
September 10 · 5 min
Elias Sayour on the Cancer Vaccine Breakthrough
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.
September 8 · 37 min
What Single Cell Sequencing Revealed About Pulmonary Fibrosis with Nick Banovich, TGen
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 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
September 3 · 31 min
The Quest to Measure Protein Function with Polly Fordyce, Stanford
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
September 1 · 31 min
Doubling Down on Long Reads: New CEO of PacBio, Mark Van Oene
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.” This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
August 27 · 4 min
Serge Saxonov on What’s Next for 10x Genomics
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.
August 20 · 5 min
“You Cannot Discover What You Cannot Make”: Lee Cronin on Programming Chemistry
This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.
August 18 · 47 min
All of Us Comes of Age. And So Does Its Funding: Josh Denny on the Next Phase of Precision Medicine
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
August 13 · 41 min
Season Opener: Is Biology Having Its Newtonian Moment? with Rob Phillips, Caltech
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.” This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
August 6 · 32 min
From the Archives: Erdinc Sezgin and the Physics of Living Cells
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
July 30 · 23 min
From the Archives: How Cellanome Is Changing the Way We Study Cell Function with Matthew Spitzer and Pier Federico Gherardini
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe
July 9 · 29 min
Agilent and Oxford Nanopore Discuss Bringing Long Reads to the Clinic with a Customer
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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

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