by Eleanor Howe and Michael DeRan
After attending Cellarity’s medSCAI Symposium on single-cell research, AI, and data-driven drug discovery, we came away buzzing with inspiration over new possibilities for our clients at Diamond Age. The event was loaded with insights and tech designed to shake up bioinformatics and open real doors for biotechs looking to innovate.
Our biggest takeaway? The latest developments are making complex data generation far more accessible and affordable for small to mid-sized companies. We’re talking about the ability to generate exponentially more data without breaking the bank. Sophisticated experimental designs are now in reach for smaller players, and we’re here to make sure our clients get the most out of these powerful tools.
The latest developments are making complex data generation far more accessible and affordable for small to mid-sized companies
From Dr. Aviv Regev’s trailblazing work on compressed CRISPR screens to Dr. Cole Trapnell’s “Hook” for tracking cell-type dynamics, these innovations aren’t just flashy tech—they’re game-changers. Suddenly, smaller biotechs can run complex, high-value experiments affordably. Imagine running a genome-wide CRISPR screen on a lean budget. Or tracking cell population changes over time, transforming static data into dynamic narratives of cellular transformation. These are exactly the types of technologies we’re testing in-house to give our clients the advanced analytical capabilities and strategic support they need to level up their experimental design.
The AI tools showcased at medSCAI represent a step up in sophistication compared to earlier applications like ChatGPT for cell type annotation. Take “Similarity City” from Regev’s lab—it’s like a search engine for your cells, using a global data atlas to reveal connections across datasets instantly. It saves time, improves accuracy, and is a serious asset for projects with large datasets.
Machine learning is where innovation is actually taking place
The symposium also gave a refreshingly grounded take on AI’s role in drug discovery. Dr. Mark Murcko’s key message: While “AI” gets all the buzz, machine learning is where innovation is actually taking place. Machine learning is done with Python; AI with Powerpoint—a playful jab at how those who do the real work call it machine learning, while others call it AI. We couldn’t agree more. AI isn’t a magic wand; it’s an ever-expanding collection of a diverse set of tools, some of which might be good for the problem at hand.
Clinical applications of AI, for instance, hold undeniable transformative potential, though they’re still finding their footing as limited training data remains a key hurdle. As AI continues to advance, it’s on course to deliver reliable clinical predictions that could reshape the field entirely.
At Diamond Age, we’re committed to more than just keeping up with trends—we’re all about turning these insights into actionable strategies that help our clients think bigger, take on ambitious projects, and get faster, more meaningful results. Curious about what these and other technologies could mean for your research, and how best to design these experiments? Reach out, and let’s turn your data into discoveries.