November Roundup
Predictive validity, Beyond the endless frontier, and Brain tissue freezing.
As with many other people, Sam Enright’s links posts have motivated me to start my own monthly links series. These posts will include personal updates as well as blog posts, podcasts, books, projects, and scientific papers I’ve been consuming in the last month. Enjoy my first links post!
My deep dive into The Making of the Electron Microscope was published in Asimov Press last month. This piece was challenging to write: it weaves together multiple inventors, the scientific environment that made the microscope possible, and its uneven path to commercialization, not to mention that I began the project knowing very little about electron microscopy. I always love working with the Asimov team. I feel myself transforming as a writer every time I go through their editing rounds. (The cover photo for this post is used in the Asimov piece and credited to David Goodsell.)
As a Roots of Progress Institute Fellow, I attended the 2025 Progress Conference in October. I was honored to write an essay reporting on the metascience track for Big Think’s special issue, The Engine of Progress. I wrote about how, since the official launch of Progress Studies in 2019, the metascience community has grown from diagnosing the stagnation of science into experimenting with new research organizations, funding models, and policies. I love that the metascience community within the progress movement is truly a group of builders taking real action.
I highly recommend reading the other fellows’ reports on the different conference tracks: Afra Wang on American Dynamism, Laura Mazer on Longevity, Jeff Fong on Policy, Grant Mulligan on Climate and Energy, and Anton Leicht on AI Protopia.
I’ve also been excited about Seemay Chou’s work at Astera in rebuilding the X-ray crystallography analysis pipeline. The Diffuse Project, which includes a team at Astera as well as investigators across universities, has intentionally deprioritized journal publication in favor of building comprehensive datasets as the primary output. Here’s the blog post. Here’s the Diffuse Project website. I hope we see many more efforts like this. High-quality, well-curated “ground truth” data is a bottleneck for building many more useful AI systems in biology.
That said, creating more datasets doesn’t solve everything. Stuart Buck makes an important point about which data we collect and how it’s structured. Data without the right metadata, like in the Childhood Cancer Data Initiative, can be far less useful than it looks. As Buck writes: “A takeaway here: If NIH wants to upscale a pediatric cancer database so that it is most useful to AI researchers, it needs to work closely with top medical and AI researchers who will have the best on-the-ground insights as to what would make the database actually useful.”
I’ve also appreciated some more measured takes on AI in biology. I found Claus Wilke’s essay, We still can’t predict much of anything in biology, a refreshing take on how far we’ve come and how far we still have to go in using AI to “solve biology.” Ruxandra Teslo writes about Eroom’s law and the need for predictive validity in our AI agents for drug discovery. Similarly, I came across Amarda Shehu’s essay, From Blue Sky to Ground Truth: Predictive AI for Life.
I enjoyed reading Beyond the Endless Frontier: Rebuilding Corporate Research for a Stronger American Future by Jeffrey Tsao. I had the opportunity to meet and spend some time speaking with Jeff at the Progress Conference. Jeff makes the case that the scientific method is incomplete, that industrial research labs like Bell Labs, Xerox PARC, and GE once created unique environments where real-world problems sparked foundational discoveries. Without such labs today, this crucial method of discovery, what Donald Stokes called Pasteur’s Quadrant research, is largely missing.
The Institute for Progress recently relaunched their Macroscience Blog. “We are relaunching Macroscience with a broader focus and community of writers, including the scientists and policy entrepreneurs shaping the future of American science. Macroscience will explore ideas for how to improve science and policy, and it’ll feature writers who challenge assumptions and start friendly arguments.”
I enjoyed Owl Posting’s podcast episode with Ellen Zhong, a computer science professor at Princeton working on machine learning for cryo-EM structure prediction. What stood out to me most was how grounded her answers were in biological reality. One thing I wanted to add, today’s high-end electron microscopes cost 10-15 million dollars.
Lots of insights in John Collison’s podcast with Eli Lilly’s CEO Dave Ricks. I’m amazed at the fact that Eli Lilly’s R&D budget is one-third of the entire NIH budget! This was also the first time I had heard in a well articulated way why clinical trials are so expensive.
Now, onto some scientific papers I’ve been reading:
For some context: in my day job, I study cellular mechanisms of neurodegeneration in Alzheimer’s and Parkinson’s disease. In both conditions, misfolded proteins (tau in Alzheimer’s and alpha-synuclein in Parkinson’s) are defining pathological features. Traditionally, the structures of these proteins are studied by extracting and purifying them from brain tissue, then applying techniques like nuclear magnetic resonance or cryo-electron microscopy to determine their atomic structure.
But to truly understand how these proteins damage cells, we need to see them in their native context, which is embedded inside real neurons, interacting with membranes, organelles, and other proteins. Cryo-electron tomography (cryo-ET) is currently the only method capable of imaging molecular structures in intact tissue at sub-nanometer resolution. Setting this up in human tissue is extraordinarily difficult, and it’s what I’m building toward in our lab.
Here are a few papers that have influenced my workflow:
In this paper, René Frank’s lab at the University of Leeds investigates the cellular architecture surrounding amyloid-beta and tau proteins in Alzheimer’s disease in what I think is an impressively ambitious technical undertaking. The team begins with flash-frozen blocks of human brain tissue, which are thawed to room temperature and kept in preservative media to prevent structural degradation. The tissue is sectioned into thin slices (~100 micrometers) and fluorescently labeled to highlight amyloid deposits. These labeled sections are then high-pressure frozen to lock native cellular structures in place.
Next, the frozen sections are imaged using fluorescence microscopy to identify amyloid-rich regions. This correlative step is essential without which locating pathology under an electron microscope would be a needle-in-a-haystack problem. Once regions of interest are identified, the samples must be thinned further. Because electrons can only pass through extremely thin specimens, the ~100-micrometer sections are trimmed down to ~70 nanometers using a diamond knife, over 1,000 times thinner than the original tissue slice. Finally, the ultrathin sections are placed in a transmission electron microscope, where images are acquired across multiple tilt angles and computationally reconstructed into three-dimensional volumes. From these reconstructions, individual tau filaments are digitally segmented and thousands of copies are aligned and averaged to reveal their molecular structure within the native cellular environment.
This study shows that amyloid-beta plaques and tau filaments in Alzheimer’s disease have distinct, spatially organized structures, and that tau filaments adopt location-specific conformations within native tissue. This study links molecular structure directly to cellular context and pathology.
There are two main ways to flash-freeze (vitrify) biological samples for cryo-EM and cryo-ET. The first, and more common method, is plunge freezing, where a sample is rapidly dropped into liquid ethane cooled to about -180C. Because the cooling happens so quickly, water in the sample does not have time to form ice crystals and instead freezes into amorphous (glassy) ice. This matters because crystalline ice is disastrous for structural biology: ice crystals expand and physically tear apart delicate cellular structures. The main limitation of plunge freezing is sample thickness, where only very thin samples, typically less than ~10 µm, cool fast enough throughout to avoid crystal formation. Thicker samples freeze too slowly in their interior, leading to crystalline ice and structural damage.
The second method, high-pressure freezing, is designed for thicker samples. Here, the specimen is rapidly cooled to liquid nitrogen temperatures (~-180 °C) while simultaneously being pressurized. Under high pressure, water is prevented from expanding as it freezes, which strongly suppresses crystal formation even in much thicker tissue. The downside is that high-pressure freezers are expensive, complex, and far less widely available than plunge freezers, putting them out of reach for many labs.
These practical constraints have pushed researchers to innovate around hardware limitations. In this paper, Yi-Wei Chang’s group at the University of Pennsylvania demonstrated that even a 100 µm-thick brain section can be plunge frozen and later processed for cryo-ET by thinning it down with a plasma focused ion beam. Using a xenon plasma beam, they were able to mill away most of the frozen tissue and produce electron-transparent lamellae just a few hundred nanometers thick. This approach effectively reopens plunge freezing as an option for much thicker biological samples by shifting the burden from freezing to post-processing.
One of the major challenges with plunge-freezing thick brain tissue is that the cold does not penetrate the interior fast enough to prevent ice crystals from forming. Instead of vitrifying into amorphous ice, water in the center of the sample partially crystallizes, damaging fine cellular and molecular structures. A common workaround is to soak the sample in cryoprotectants, which are chemicals that slow ice nucleation and crystal growth. But not all cryoprotectants are equal: some preserve structure at the cost of massively increasing osmolarity, while others risk chemically perturbing proteins or membranes. In this paper, Weier et al. systematically benchmark different cryoprotectant cocktails to identify mixtures that can help vitrify brain tissue up to ~100 µm thick by plunge freezing alone, without destroying physiological structure.
I have many more papers I could share, but these summaries ended up much longer than I intended, so I’ll stop here and pick up again next month.
Finally, I’ll be attending Foresight Institute’s Vision Weekend in SF this upcoming weekend.
Happy Thanksgiving!