Ion Genomics Podcast

Episode 15: OHSU Researcher Andrew Adey discusses his interest in methylation and how researchers are using new technologies to fill in theories about cell identity and aging dating back to  the 1950s. 

For more stories, podcasts, and other coverage of genomics, visit www.iongenomics.bio.

Eric Topol Substack Article: “Loss of Cell Identity Drives Human Aging”

Review paper on epigenetics and aging:

Yücel, A.D., Molière, A. & Gladyshev, V.N. “Written in development and lost in ageing: the grammar of cellular identity.” Nature. Sept. 30, 2026.

Another paper on epigenetics and aging

Lu et al. “Prevalent mesenchymal drift in aging and disease is reversed by partial reprogramming.” Cell. October 2025.

Adey lab preprint on single-cell methylation analysis in the brain:

Rylaarsdam, et al. “Early postnatal DNA methylation dynamics define neuronal subtypes and are disrupted by MECP2 loss.” bioRxiv. June 8, 2026.

Adey lab Amethyst paper:

Rylaarsdam, et al. “Single-cell DNA methylation analysis tool Amethyst resolves distinct non-CG methylation patterns in human astrocytes and oligodendrocytes.” Commun Biol. Oct. 14, 2025.

Ecker lab paper on non-canonical methylation in the brain:

Lister, et al. “Global epigenomic reconfiguration during mammalian brain development.” Science. July 4, 2013. 

My GenomeWeb story on methylation sequencing tech (paywall)

SciMet v3 paper:

Nichols, et al. “Atlas-scale single-cell DNA methylation profiling with sciMETv3.”Cell Genomics. Jan. 8, 2025. 

Grail’s Galleri blood-based cancer detection test appears headed towards approval by the FDA after it passed a trio of votes from an advisory committee Sept. 23. However, a closer look at the votes — especially the split vote on the question of efficacy — and the debate that preceded it revealed just how much remains unsettled about what the test achieves. As this is the first test of its kind, with more to follow, how FDA chooses to regulate multiple aspects will reveal a lot about where the field is and what it still may need to prove.

And it may be the last chance science-minded regulators have to put some reins on Galleri before it gets out into a world where Grail has used its money and influence to tilt the market in its favor. 

Alex Dickinson, a former Illumina executive who has built a large following on LinkedIn, in part due to his sharp critiques of Grail and Galleri, joined me to break the situation down.

Alex Dickinson’ LinkedIn Feed

OpenOnco

Gilbert Welch STAT Op-ed

https://www.statnews.com/2026/10/06/grail-multi-cancer-screening-test-criticism/

AdCom Vote Explanation Segment Transcript, including Dan Swerdlow's quote on the burden of Galleri on the US healthcare system

https://www.iongenomics.bio/p/raw-reads-grail-galleri-fda-adcom?r=3ihla5

My story for GenomeWeb on Grail disclosing that the NHS-Galleri trial not meet its primary endpoint (Paywall)

https://www.genomeweb.com/cancer/grail-fda-plans-undeterred-after-nhs-galleri-mced-trial-misses-primary-endpoint/

David Cameron NHS/Grail/Illumina 

https://www.bbc.com/news/business-58146567

https://www.bmj.com/content/386/bmj.q1706

Grail’s $15 million in lobbying

https://www.iongenomics.bio/p/grails-15m-lobbying-shortcut-newsletter?r=3ihla5

Grail participation in RFK Jr.-linked event, first reported by Bloomberg (paywall)

https://www.bloomberg.com/news/articles/2026-09-18/rfk-jr-linked-group-offered-access-to-us-officials-for-payment

And also in the Los Angeles Times

https://www.latimes.com/science/story/2026-09-22/rfk-jr-linked-group-offered-access-to-us-officials-for-fee

NHS Oncologists’ Letter to The Lancet 

“GRAIL-Galleri: why the special treatment?”  

Episode 13

China’s burgeoning biotech and genomics markets make for the ultimate compare and contrast. 

Like the US, it has regional hotspots, intense competition, and a big company whose success has helped seed a second wave of startups, many of whom are now breaking through. 

However, the clinical system is both vast and vastly different, marketing approaches can seem stuck in the past, and success abroad — rather than at home — may end up being the difference between making it and getting caught in the churn. 

That’s according to Matthew Marlowe, who runs BridgCross Bio, a digital publication seeking to provide insight into China’s biotech industry for a Western audience. Earlier this month, Marlowe spent two weeks traveling across China to meet with and discover companies based there. 

We discuss some of the new tech he’ll be watching and what it means for companies based in the US and Europe, especially Illumina, which has been hamstrung by high-level government policy that has made it difficult to grow revenue there. 

Make sure to listen all the way through for a new tidbit I’ve extracted about Illumina CEO Jacob Thaysen’s May trip to China at the invitation of the White House. 

Marlowe is also founder of Genapse, a marketing company for life science companies.

Nicholas Larus-Stone is Benchling’s Head of AI and coauthor of a new benchmarking study putting large language models (LLMs) like Claude and ChatGPT to work redesigning wet lab protocols. 

The models did just fine, but didn’t blow the Benchling team away. “They can certainly be helpful at certain biological tasks, but they've by no means solved some of the kind of biology problems that people really care about,” Larus-Stone said.

That said, he believes that pharmaceutical companies should be spending more on AI usage. That’s just one of several spicier takes we got into in our conversation. 

Listen for more of Larus-Stone’s thoughts on why benchmarking LLMs is important, how open-source models can be competitive against proprietary ones, how he has fostered cross-pollination between software engineers and scientists by founding Bits in Bio, and whether he likes the term “TechBio.” 

Benchling’s Bench-Bench protocol study white paper

Benchling blog post about the benchmarking study

Bits in Bio Website

Bits in Bio 2025 Annual Report

Other AI benchmarking studies 

From Jure Lescovec’s lab and the Biomni team:

Qu et al. “BiomniBench: Process-level Evaluation of LLM Agents for Real-world Biomedical Research” bioRxiv. May 12, 2026.

Guo et al. “PromptBio-Bench: Benchmarking LLM-based Bioinformatics Agents for End-to-End Data Analysis.” BioRxiv. June 1, 2026. 

Other podcast episodes referenced

https://www.iongenomics.bio/p/ai-agents-are-coming-for-the-lab

Gordon Freedman and I both developed an interest in mitochondria due to our love of cycling and to understand the limits of performance. Whereas I sought to find a racer’s edge by delving into the biology underlying endurance sports, Freedman sought answers to a simpler question: Why can’t I push as hard as I used to be able to?

Follow us down the rabbit hole to a world where there are multiple genomes to consider — one of them bacterial in nature — and a dearth of experimental tools has made it hard to connect mitochondria and their functions to other questions in biology and health. But there’s also hope that by connecting mitochondria to research areas such as cancer and neurodegenerative disease, scientists can peer further into the darkness than they could with only the light of the nuclear genome. 

Show notes: 

Gordon’s website, MitoWorld

Distribution of pools of mitochondria in skeletal muscle cells. Image credit: Parry et al. 

Parry et al. “Impact of Capillary and Sarcolemmal Proximity on Mitochondrial Structure and Energetic Function in Skeletal Muscle.” The Journal of Physiology. April 2, 2024.

Guo, Y., Xue, Y. Mitochondria as convergence hubs for innate immunity pathways. Communications Biology. (June 8, 2026).

Shen et al. “Mitochondria as Cellular and Organismal Signaling Hubs.” The Annual Review of Cell and Developmental Biology. July 8, 2022.

IMDB page for A Brief History of Time.

https://www.imdb.com/title/tt0103882/

How are people fighting ongoing epidemics using genomics-based technologies such as PCR diagnostics and mRNA vaccines in a world where funding from the US and other rich countries is on the decline? 

My guest this week, my dear friend and fellow journalist William Herkewitz, has reported on the recent Ebola outbreak in the Democratic Republic of Congo as well as tuberculosis around the world. A former communications director for the now defunct USAID in multiple African countries, he has seen firsthand how funding from the US helped keep people healthy and infectious diseases at bay. 

We discuss what happens when dollars disappear, and with them, networks of people and trust. We also evaluate PCR, next-generation sequencing, and paper-based diagnostic tests in these remote and resource-limited settings. 

For more of Will’s reporting, check out The Rift Valley Dispatch, also on Substack. 

https://www.theriftvalleydispatch.com/

Show notes: 

Will's New York Times Op-ed

“Why We Couldn't Sell America on U.S.A.I.D.”

“Rollout of 5,000 Near Point-of-Care TB Test Systems in 13 Countries Begins With $50M in Funding,” 

by Madeleine Johnson

What happens when scientists come together and realize they didn’t have the data to back up their assumptions?

Dino Di Carlo, my guest on this week’s Ion Genomics podcast, shares how he reacted when he found out that there were huge gaps in understanding how individual cells communicate with each other. To remedy this, he has proposed a project to analyze a billion cells, largely in two-cell dyads. He published a paper outlining his vision in Nature Biotechnology. 

His idea leverages two sequencing technologies. One is proximity sequencing (Prox-seq), a method developed at the University of Chicago that offers information on proteins near each other on top of standard single-cell transcriptomics. 

Another, developed in Di Carlo’s lab and being commercialized by a company he cofounded, is nanovials, a cell capture method that enables the analysis of cell dyads using popular single-cell sequencing techniques. The tech is being commericalized by Partillion Bioscience, a company Di Carlo cofounded.

Di Carlo spoke to me from Japan, where he’s spending the summer setting up a satellite lab, in large part to find stability after he was denied access to grant funding his lab had been awarded as part of the Trump Administration’s conflict with major research universities. Stay tuned to the end for his recommendations on the most underrated sushi types on the menu. 

My guest this week, Johnny Yu, has seen firsthand how the new code-writing capabilities of LLMs has widened horizons for the use of AI in biology. Tahoe Bio, a company he cofounded, has built its own AI agents, which are helping it rethink how to use AI in drug discovery and development.

We discuss two other recent agentic AI developments from recent weeks. A publication in Science from Jure Leskovec's Stanford University Lab on Biomni, a multi-purpose, general intelligence that he hopes will help researchers get more out of their efforts.

“What if every biomedical scientist had a tireless expert collaborator?," he said. "One that could actually go read literature, one that could actually go run the analysis, understand the data, design the experiments across any area of biology?”

Started in 2024 and put up as an open-source project in 2025, Biomni — already being commercialized by a startup, Phylo — heralds a new wave of technologies that stand on the shoulders of the large language models behind products like Google Gemini and Anthropic's Claude. 

Just last week, Anthropic launched Claude Science, another wide-ranging research aide that is also based on AI agents. And a recent leap in Claude’s ability to write code means even more are likely on the way.

Researchers are already lining up to try them out. According to Leskovec and his Phylo cofounders Kexin Huang and Jerry Qu, more than 10,000 people have already started using Biomni.

For more, check out the post on iongenomics.bio.

In 2024, Sid Sijbrandij got the news that no cancer patient wants to hear: his tumor was back and his doctors told him they had nothing left to offer. 

Sid, a tech entrepreneur who up until that point had been running his company, GitLab, went all in, doing as much testing and as many treatments as he could. 

It seems to have worked. Moreover, Sid’s team thinks they’ve been able to figure out what went right, thanks to heaps of molecular data they’ve collected, including single-cell sequencing data.

“It's very difficult, if not impossible, to do causative work in an N-of-1 study, but because we have this longitudinal sampling over the course of Sid's case, we can start to build a mechanistic hypothesis,” said Jacob Stern, a single-cell data expert on Sid's team and veteran of 10x Genomics. “Which seems to be that there was a bunch of combination immunotherapy that helped to rev up the immune system over the course of 2024.”

Join Sid, Jacob, and I as we discuss the "maximal diagnostics” approach and how Sid plans to bring it to other patients who find themselves in a similar situation.During our chat, Sid mentions that he got a treatment from a company started by a friend he made at the Y Combinator startup accelerator. That company is Shasqi, founded by José Oneto.

To see a timeline of Sid’s experience and all the data he and his team collected diagnosing and treating his disease check out osteosarc.com.

Sid has also written about his experience on his personal Substack.

For more information about Sid’s policy proposals, check out slide 17 of his embedded PowerPoint at https://sytse.com/cancer/

For more information about the cancer “playbooks” offered by the Sijbrandij Foundation’s Future of Cancer Care Today program, see:

https://sijbrandijfoundation.org/fcct

More information about the companies Sid is investing in to bring aspects of his maximal diagnostics approach to market is at Evenone.ventures.

“We right now are developing our technology to be able very soon to sequence roughly hundred-length proteins that are on each of those sensors within a 24-hour period. You can think about this as being essentially 10 billion letters per day: 100 million sensors times those 100-mers on each sensor and then sequencing letter by letter.”

That’s the scale that Pumpkinseed CEO and Cofounder Jen Dionne thinks her company can reach for analyzing proteins. The technology, developed in her lab at Stanford University, uses Raman spectroscopy to fingerprint molecules and Edman degradation to achieve sequencing-by-subtraction of peptides: 30 amino acids at a time now, possibly up to 300, or full-length proteins, in the future. 

The ability to capture 10 billion amino acids per day would represent several orders of magnitude better throughput than the best and fastest proteomics methods available today, whether via mass spectroscopy or other single-molecule technologies being developed by competitors like Quantum-Si or Nautilus. 

Remarkably, that’s still a long way from achieving the data acquisition rate that state-of-the-art high-throughput sequencers can achieve, but it’s enough to start thinking about making the type of impact that the first next-generation sequencers had on biological research. 

Join us as we discuss the underlying physics behind Pumpkinseed’s technology, Jen’s journey from lab to C-suite, the applications they’re already pursuing, and her love of sports, including the World Cup. 

For more information, check out pumpkinseed.bio or email Jen. 

Links to papers discussed in the episode:

Hu et al. Rapid genetic screening with high quality factor metasurfaces. Nature Communications, July 26, 2023.

Zhang et al. From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis. ACS Nano, July 1, 2024.

Stiber, et al. Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy. BioRxiv, February 23, 2026.