User:Nikolai Slavov

Contact Info

Nikolai Slavov

• Slavov Laboratory
• Slavov Lab blog
• Faculty Profile
• Mugar Life Sciences Building, Office 334
• 360 Huntington Avenue, Boston, MA 02115
• https://alum.mit.edu/www/nslavov
• Email via OpenWetWare

I received my undergraduate education from MIT in 2004. Then, I pursued doctoral research in the Botstein laboratory at Princeton University, aiming to understand how cells coordinate their growth, gene expression, and metabolism. During my postdoctoral research in the van Oudenaarden laboratory at MIT, I characterized trade-offs of aerobic glycolysis (also known as Warburg effect). Subsequently, I obtained direct evidence for differential stoichiometry among core ribosomal proteins in unperturbed wild-type cells. These results suggest that specialized ribosomes regulate protein synthesis. This new regularly layer is a research focus of my laboratory at Northeastern University. Another focus is the development of quantitative mass-spectrometry methods for single cell proteomics and metabolomics.

Education

• 2010, PhD, Botstein Lab, Princeton University
• 2006, MS, Princeton University
• 2004, BS, Massachusetts Institute of Technology

Research interests

Ribosome-mediated translational regulation

All living cells must coordinate their metabolism, growth, division, and differentiation with their gene expression. Gene expression is regulated at multiple layers, from histone modifications (histone code) through RNA processing to protein degradation. While most layers are extensively studied, the regulatory role of specialized ribosomes (ribosome code) is largely unexplored. Such specialization has been suggested by the differential transcription of ribosomal proteins (RPs) and by the observation that mutations of RPs have highly specific phenotypes; particular RP mutations can cause diseases, such as cancer and Diamond Blackfan anemia, and affect selectively the synthesis of some proteins but not of others. This selectivity and the differential RP transcription raise the hypothesis that cells may build specialized ribosomes with different stoichiometries among RPs as a means of regulating protein synthesis. While the existence of specialized ribosomes has been hypothesized for decades, experimental and analytical roadblocks (such as the need for accurate quantification of homologous proteins and their modifications) have limited the evidence to only a few examples, e.g., the phosphorylation of RP S6. We developed methods to clear these roadblocks and obtained direct evidence for differential stoichiometry among core RPs in unperturbed yeast and mammalian stem cells and its fitness phenotypes. We aim to characterize ribosome specialization and its coordination with gene regulation, metabolism, and cell growth and differentiation. We want to understand quantitatively, conceptually, and mechanistically this coordination with emphasis on direct precision measurements of metabolic fluxes, protein synthesis and degradation rates in absolute units, molecules per cell per hour.

Single-Cell ProtEomics by Mass-Spectrometry (SCoPE-MS)

Cellular heterogeneity is important to biological processes, including cancer and development. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, our laboratory developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.

Publications

1. Malioutov D., Chen T., Jaffe J., Airoldi E., Carr S., Budnik B., Slavov N. (2017)

   Quantifying homologous proteins and proteoforms

   bioRxiv PDF | HIquant

2. Budnik B., Levy E., Slavov N. (2017)

   Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

   bioRxiv PDF | Data Web

3. Franks A., Airoldi E., Slavov N. (2017)

   Post-transcriptional regulation across human tissues

   bioRxiv PDF | 10.1371/journal.pcbi.1005535 | Data Web

4. Slavov N., Semrau S., Airoldi E.M., Budnik B., van Oudenaarden A. (2015)

   Differential stoichiometry among core ribosomal proteins. Cell Reports

   PDF

   Indirect evidence gathered over decades has suggested the existence of ribosomes with distinct protein composition and translational specificity in unperturbed wild-type cells. Slavov and colleagues report direct evidence for such ribosome heterogeneity in yeast and mouse stem cells and correlative evidence for its physiological impact on cell growth.

5. Slavov N., Botstein D., Caudy A. (2014)

   Extensive Regulation of Metabolism and Growth during the Cell Division Cycle. bioRxiv

   PDF

6. Malioutov D., Slavov N. (2014)

   Convex Total Least Squares.

   JMLR, W&CP vol. 32, PDF

   The ordinary-least-squares, commonly known as regression, assumes that the independent variables are measured without error. However, most empirical measurements have varying degree of noise. Ignoring such variable noise in the the independent variables can result in large systematic errors in the inference even in the limit of infinite data. This article describes a principled solution (inference algorithm) for such problems, known as structured total-least-squares.

7. Slavov N., Budnik B., Schwab D., Airoldi E.M., van Oudenaarden A. (2014)

   Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis.

   Cell Reports, vol. 7 PDF

   We find that exponential growth at a constant rate can represent not a single metabolic/physiological state but a continuum of changing states and that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival.

8. Slavov, N., Carey, J., Linse S. (2013)

   Calmodulin transduces Ca+2 oscillations into differential regulation of its target proteins.

   ACS Chemical Neuroscience, vol. 4, issue 2 PDF

   The molecular and network properties of the calmodulin signaling network, combined with its lignad-binding dynamics, can transduce a common signal (calcium levels) through a common signaling hub (calmodulin) and yet send different signals to many downstream proteins.

9. Slavov N. and Botstein D. (2012)

   Decoupling Nutrient Signaling from Growth Rate Causes Aerobic Glycolysis and Deregulation of Cell Size and Gene Expression

   Mol. Biol. Cell, vol. 24, no. 2 PDF

   The nutrition and the growth rate of a cell are two interacting factors with pervasive physiological effects. Our experiments decouple these factors and demonstrate the role of a growth rate signal, independent of the actual rate of biomass increase, on gene regulation, the cell division cycle, and the switch to a respiro-fermentative metabolism.

10. Slavov N., van Oudenaarden A.*(2012) How to Regulate a Gene: To Repress or to Activate? Mol. Cell, vol. 46, issue 5, 551-552

    PDF

11. Slavov N., Airoldi E., van Oudenaarden A., and Botstein D. (2012)

    A Conserved Cell Growth Cycle Can Account for the Environmental Stress Responses of Divergent Eukaryotes

    Mol. Biol. Cell, vol. 23, no. 10, 1986-1997 PDF

    We find that transitions between the two phases of the cell growth cycle can account for the environmental stress response, the growth-rate response, and the cross protection between slow growth and various types of stress factors. We suggest that this mechanism is conserved across budding and fission yeast, and normal human cells.

12. Slavov N., Macinskas J., Caudy A., Botstein D. (2011)

    Metabolic Cycling without Cell Division Cycling in Respiring Yeast

    PNAS, vol. 108, no. 47, 19090-19095 PDF

13. Slavov N. and Botstein D. (2011)

    Coupling among Growth Rate Response, Metabolic Cycle and Cell Division Cycle in Yeast

    Mol. Biol. Cell, vol. 22, 1997-2009 PDF

    We discovered that the relative durations of the phases of the yeast metabolic cycle change with the growth rate. These changes can explain mechanistically the transcriptional growth-rate responses of all yeast genes (25% of the genome) that we find to be the same across all studied nutrient limitations in either ethanol or glucose carbon source.

14. Slavov N. (2010)

    Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks, JMLR, vol. 9

    PDF

15. Silverman SJ., Slavov N., Petti A., Parsons L., Briehof R., Thiberge S., Zenklusen D., Gandhi SJ., Larson D., Singer R., Botstein D. (2010)

    Metabolic cycling in single yeast cells from unsynchronized steady-state populations limited on glucose or phosphate

    PNAS, vol. 107 PDF

    F1000 Review

16. Slavov, N.✉, Dawson, K. (2009)

    Correlation Signature of the Macroscopic States of the Gene Regulatory Network in Cancer

    PNAS, vol. 106, no. 11 PDF

Protocols

• Phosphate-Limited Medium with Ethanol as a Sole Source of Carbon and Energy[1]
• Glucose-Limited Mineral Medium[2]
• Ethanol-Limited Mineral Medium[3]