Slavov Laboratory

We seek principles in the coordination among protein synthesis, metabolism, cell growth and differentiation PI: @slavov_n Videos:

[Slavov Laboratory @slavovLab]

We report many proteins not predicted by the genetic code.

They are stable & abundant O( 10³ ) copies / cell.

Generative mechanisms include codon-anticodon mismatches & RNA modifications.

Their abundance depends on codon frequency & protein stability.

https://t.co/lAxvulC4zx

[Prof. Nikolai Slavov @slavov_n]

A video summary of our recent preprint

https://t.co/vrhzMFJxXy

[Prof. Nikolai Slavov @slavov_n]

How much data would it take to describe human biology ?

A very crude description of molecular abundance requires 1,000× more bites than all digital data humanity stores.

We need more than scale to close the gap.

https://t.co/t37I4HqmoY

[Prof. Nikolai Slavov @slavov_n]

Using single-cell proteomics, we discovered protein (and functional) symmetry breaking in early mammalian embryos influencing cell fates.

These proteomic asymmetries are detectable at the zygote stage, intensify by the 2-cell and 4-cell stage, and correlate with the sperm entry site, pointing to fertilization as a symmetry-breaking event. The clear protein differences allowed us to define two types of blastomeres, termed alpha and beta.
⬛ These differences predict the developmental potential of the blastomeres. Similar clustering and protein enrichment patterns found in human 2-cell embryos suggest this early asymmetry might be conserved.

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[Prof. Nikolai Slavov @slavov_n]

Beyond heterogeneity, single-cell analysis can reveal functional coherence.

Coherence, regularity and principles advance science.

Full recording: https://t.co/LJNpi3Z7iG

[Prof. Nikolai Slavov @slavov_n]

Millions of PBMCs have been analyzed by scRNA-seq. Have we seen it all ?

We profiled thousands of single PBMCs — proteomes and transcriptomes side by side — and uncovered functional coordination undetectable in the deepest scRNA-seq we could perform.

https://t.co/JoozcYoBrm

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[Slavov Laboratory @slavovLab]

A very thoughtful and insightful discussion with @JShendure.

Most of it focuses on the development of DNA sequencing by synthesis, but the ideas and lessons from that experience are general and applicable to other technologies.

https://t.co/tAMOH0IiyG

[Prof. Nikolai Slavov @slavov_n]

Among the numerous metrics evaluating academics, one stands out for me:

Our intellectual heritage reflected in our students and colleagues, and their intellectual growth.

That amplifies the initial sparks and bears more fruit than any one of us could have produced.

[Slavov Laboratory @slavovLab]

Prof. Jonathan Sweedler opened this discussion with a fantastic question:

https://t.co/ffNiFa6j8q

[Prof. Nikolai Slavov @slavov_n]

Some of the most important scientific results were initially rejected before they became important milestones of scientific progress.

🔷 If you have great results, this is one of the most effective ways to help them succeed: https://t.co/s2Ndib4o0j

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[Slavov Laboratory @slavovLab]

@ReSyn_Bio Thank you for the highlight of JMod.

Its performance is very impressive. We are super excited to use it, though we didn't develop it. It was developed at @ParallelSqTech.

[Prof. Nikolai Slavov @slavov_n]

Nature just published a tech feature on single-cell proteomics.

It highlights our journey from skepticism to robust technology and biological discoveries.

I love that the feature starts with a biological discovery enabled by the technology: https://t.co/LqzouYfg9x

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[Prof. Nikolai Slavov @slavov_n]

@ItaiYanai My personal experience is different.

I always felt motivated to take risks, and my most recognized success is based upon taking risks.

I recently talked about this experience: https://t.co/AbS3ZKV9MK

[Prof. Nikolai Slavov @slavov_n]

High-throughput omics are powerful, but they don’t substitute for thinking.

Discovering new biology often starts with intellectual input: frame sharp questions first, then choose the methods that can answer them, not the one that’s most convenient or currently fashionable.

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[Prof. Nikolai Slavov @slavov_n]

My personal experience is different.

I always felt motivated to take risks, and my most recognized success is based upon taking risks.

I recently talked about this experience: https://t.co/AbS3ZKV9MK

[Prof. Nikolai Slavov @slavov_n]

What sets protein gradients in the liver ?

The latest single-cell analysis from @ParallelSqTech shows it's often spatially regulated rates of protein synthesis and degradation.

Liver cells (hepatocytes) form a gradient from the portal to the central vein, with variable protein abundance along this axis.

Based on in vivo metabolic pulses with isotope labeled amino acids, we found zonated proteins whose mRNA levels don't track their protein levels: Their degradation rates do.

A clear example is Mttp, a lipid-transfer protein that rises from portal to central cells. Its RNA doesn't explain that gradient. Its degradation rate does: Mttp is broken down faster in portal cells and more slowly in central cells.

🔷 The spatial pattern is set by protein stability.

Zooming out, this wasn't a one-off. Across functional categories, zonated protein half-lives lined up with zonated protein abundance — a systematic signature of degradation shaping spatial identity.

Interestingly, portal cells showed globally longer protein half-lives than central cells, especially for long-lived proteins.

⬛ A takeaway: Spatial identity in tissues isn't just about which genes get turned on — it's also about spatially regulated rates of protein degradation. Post-translational regulation is a driver of tissue architecture.

📄 Article: https://t.co/T0zRYtAiSX

[Prof. Nikolai Slavov @slavov_n]

These numbers are key to understanding RNA and protein analysis.

The different counting statistics fundamentally shape technological challenges and opportunities.

https://t.co/fdGLradaFH

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[Prof. Nikolai Slavov @slavov_n]

These results bode well for the feasibility of massively scaling up single-cell proteomics while preserving and increasing the depth of coverage and quantitative accuracy.

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[Prof. Nikolai Slavov @slavov_n]

Accurately quantifying over 700K precursors in a single 9-plexDIA set of 20ng proteomes is a new milestone.

JMod achieves high quantitative accuracy over a 32-fold dynamic range of proteome spiked-in ratios across single-cell and bulk sample sizes.

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https://t.co/quGLcMSw9y

[Slavov Laboratory @slavovLab]

Understanding protein biology at the molecular level can unlock entirely new ways to engineer protein interactions, and with them, a new generation of powerful therapies.

[Prof. Nikolai Slavov @slavov_n]

A new pharmacological strategy -- based on engineering protein surfaces -- just achieved major success against pancreatic cancer.

It points toward a much broader class of cancer therapies.

Yesterday, the FDA approved daraxonrasib (Rasonque), a drug that targets the RAS proteins driving most pancreatic cancers. What makes it especially fascinating is how it works.

Rather than simply finding a conventional pocket on RAS, daraxonrasib binds the chaperone protein cyclophilin A and uses it to create a new protein surface. This drug–cyclophilin complex then engages active RAS, forming a three-part complex that blocks RAS interactions with downstream effectors. Remarkably, the complex can also stimulate GTP hydrolysis, further suppressing RAS signaling.

This is chemical biology at its most powerful: a small molecule is not merely inhibiting a protein: It is engineering a new protein–protein interaction inside the cell to pharmacologically control a previously “undruggable” protein.

The clinical impact is already impressive: in a randomized phase III trial, median survival in metastatic pancreatic cancer increased from 6.7 to 13.2 months compared with standard chemotherapy.

🔷 Perhaps the most exciting aspect is what comes next. If we can pharmacologically engineer new protein surfaces and interactions, rather than being restricted to naturally occurring binding pockets, the universe of druggable proteins—and potentially the range of cancers we can treat—could expand dramatically.

A beautiful example of how understanding and engineering proteins can open entirely new therapeutic possibilities.

[Prof. Nikolai Slavov @slavov_n]

Exactly 5 years ago, I presented projections for single-cell proteomics.

It's fun and instructive to reflect on the projections, progress over the last 5 years, and on their correlation.

The agreement is excellent 🚀

https://t.co/FQUChnMbyX