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]

It is a forward-looking perspective, looking at what lies ahead:
• Technology: Approaches for extending the depth and scope of measurements and scaling their throughput in ways that preserve their quality and enable cohort-scale analysis.

• Biology: The functional organization of cellular proteomes, already visible in protein covariation, protein turnover, and protein coordination shaping functional phenotypes.

@J_ASMS Perspective (open access): https://t.co/0cM8u7cHyO

Biemann Lecture (video recording): https://t.co/PjuXuO8U34

[Prof. Nikolai Slavov @slavov_n]

In 2015, single-cell proteomics by mass spectrometry was widely considered infeasible. Our first article reporting such methodology faced rejections and opposition. Once the opportunities were established, the skepticism turned into enthusiasm. A decade later, the technology has opened new perspectives towards biology.

Receiving the Biemann Medal prompted me to trace this arc: From proof of principle to increasingly powerful technologies.

https://t.co/0cM8u7cHyO

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[Prof. Nikolai Slavov @slavov_n]

Discussions of gene expression often stop at transcription: A legacy of technological limitations.

Much of the functionally important action happens later: We can now study these stages in scalable, quantitative ways, bringing us closer to understanding biology.

https://t.co/nB6ti70qE8

[Prof. Nikolai Slavov @slavov_n]

Biology advances in leaps, and the leaps follow measurement. Sequencing DNA gave us genomes and transcriptomes.

The next leap will come from embracing the molecules and processes animating life.

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https://t.co/qhr2VorMUW

[Prof. Nikolai Slavov @slavov_n]

In pursuit of this new direction, our team at @ParallelSqTech developed multiple timePlex implementations.

They demonstrate strong potential. A few highlights include:
https://t.co/59krYbayuk

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[Prof. Nikolai Slavov @slavov_n]

A major update on combinatorial scaling of proteomics throughput using time-encoded multiplexing.

Mass spec proteomics has long multiplexed samples in the mass domain (with mass tags). timePlex multiplexes in the time domain, setting a direction with new possibilities.

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[Prof. Nikolai Slavov @slavov_n]

Participating in the rapid evolution of this technology, from proof of principle to a robust, quantitative methodology, has been a thrilling adventure.

Writing this perspective revived many wonderful memories and brought a bright smile to my face.

[Prof. Nikolai Slavov @slavov_n]

Realizing this workflow for single-cell proteomics benefited from discussions at the 2025 single-cell proteomics conference.

Facilitating such collaborations is a core part of @SCP_meeting's mission !

[Prof. Nikolai Slavov @slavov_n]

Parallel Squared Technology Institute aims to scale the throughput of sensitive proteomics without compromising quantitative accuracy, depth or specificity.

Today, we share a step in this direction:
🔹 A workflow supporting the analysis of 1,130 single-cell samples per day (SPD) while preserving depth and quantitative accuracy.

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https://t.co/ygU4OhMME6

[Slavov Laboratory @slavovLab]

Protein functions depend on the full amino acid sequence, including isoforms, cleavage products, and modified residues that distinguish proteoform states.

To capture these modifications, we and @ParallelSqTech introduce multiDIA to increases proteome sequence coverage.

[Prof. Nikolai Slavov @slavov_n]

Proteomics has long faced a choice: Go deep, or go fast.

Deep, high-confidence coverage of protein sequences has traditionally required extensive chromatographic fractionation, large sample inputs and data acquisition times too long to be practical for analyzing human cohort scale samples. Scalable data-independent acquisition (DIA) workflows handle many samples quickly, but detect less of each protein and with lower confidence.

Our new preprint introduces multiDIA: It gets more from each sample without fractionation.

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https://t.co/BmTjFmIyvm

[Prof. Nikolai Slavov @slavov_n]

Proteomics has long faced a choice: Go deep, or go fast.

Deep, high-confidence coverage of protein sequences has traditionally required extensive chromatographic fractionation, large sample inputs and data acquisition times too long to be practical for analyzing human cohort scale samples. Scalable data-independent acquisition (DIA) workflows handle many samples quickly, but detect less of each protein and with lower confidence.

Our new preprint introduces multiDIA: It gets more from each sample without fractionation.

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https://t.co/BmTjFmIyvm

[Prof. Nikolai Slavov @slavov_n]

Congratulations to Luke Khoury on the successful defense of his dissertation.

Working, learning and growing together has been fun.
I am proud of his scientific growth.

Our research has opened a fresh perspective on peripheral immune cells:

https://t.co/r2azpNVFzn

[Prof. Nikolai Slavov @slavov_n]

The regulatory variation within a cell type is remarkably informative and interesting.

[Prof. Nikolai Slavov @slavov_n]

This gets particularly interesting in single cells from the same cell type.

Working at the cell type level misses much of the action.

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https://t.co/49lnT3M7MX

[Slavov Laboratory @slavovLab]

Join the Dissertation Defense of Luke Khoury:

Decoding Cell-Type-Specific Functional Coordination in Primary Human Immune Cells

Thursday, October 15, 2026 | 9:30am-11:30am EST
Richards Hall 229

[Slavov Laboratory @slavovLab]

Join the Dissertation Defense of Luke Khoury:

Decoding Cell-Type-Specific Functional Coordination in Primary Human Immune Cells

Thursday, October 15, 2026 | 9:30am-11:30am EST

Richards Hall 229

[Prof. Nikolai Slavov @slavov_n]

Do you need an intro to analyzing (prote)omics data?

This series of lectures may be helpful.
We aimed to make them short & accessible.

Enjoy watching & learning !

https://t.co/gTHe1J19Jq

[Prof. Nikolai Slavov @slavov_n]

Read the methods before reading the results
Read the methods before reading the conclusions
Read the methods before evaluating the conclusions
Read the methods before making conclusions

Read the methods!

[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