Hratch M. Baghdassarian

I am a Postdoctoral Associate in the Lauffenburger  Lab at MIT, using neural network modeling approaches to decipher immune cell signaling pathway activity at single-cell resolution. I recently received my PhD in Bioinformatics and Systems Biology in the Lewis Lab at the University of California, San Diego.  My general interests lie in systems and quantitative biology. My full CV can be found here.

Research Interests

Systems and Quantitative Biology

1. Developing analytical tools to understand multicellular systems. Leveraging omics, alongside other data-types, to study multicellular systems can reveal how individual cells and molecules confer higher-order (e.g., tissue- and organismal-scale) functions and phenotypes. Multicellular systems biology encompasses such topics as identifying ligand-receptor mediators of cell-cell communication, understanding the cell circuits responsible for homeostasis, and deciphering how extracellular cues (nutrients and intercellular signals) lead to coordinated programs of intracellular pathway activity (signaling, gene expression, and metabolism).  I am interested in developing analytical tools that can provide mechanistic insights to these concepts, with a special focus on how they change in a context-dependent manner (i.e., with time, space, and disease-state). 

2. Uncovering resource allocation principles in mammalian cells, with a focus on metabolism and secretion. Approaching biological function from a resource allocation framework--the optimization of cellular objectives under resource constraints--can provide a unique perspective and insights into intracellular pathway activity and the genotype-phenotype relationship.  I am extending mammalian genome-scale modeling approaches to link resource allocation principles across multiple biological layers. 

3. Increasing the power of single-cell analyses. As single-cell technologies become ubiquitous, we must continuously develop appropriate statistical methods to analyze them. I am interested in developing methods to assess samples that represent multiple (two or more) contexts. This includes such topics as data integration, multi-omics, and  downstream mechanistic analysis. 

4. Harnessing mechanistic and statistical modeling for therapeutics. Biological models inform and accelerate the experimental process of therapeutic discovery and design. While statistical models tend to have high predictive power, mechanistic models can provide insights to the specific key molecular features and pathways involved. I am interested in finding novel intersections between these two modeling approaches, with the ultimate goal of enhancing therapeutic design.

Publications

First Author

1. H. Baghdassarian*, D. Dimitrov*, E. Armingol*, J. Saez-Rodriguez, N. Lewis.  "Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples." Accepted, Cell Reports Methods. https://doi.org/10.1101/2023.04.28.538731.

2. H. Baghdassarian, N. Lewis. "Resource Allocation In Mammalian Systems". Biotechnology Advances. (Jan. 2024). https://doi.org/10.1016/j.biotechadv.2023.108305. (Free Link).

3. H. Baghdassarian*, S. Blackstone*, O. Clay, et al . "Variant STAT4 and Response to Ruxolitinib in an Autoinflammatory Syndrome." NEJM. (May 2023). https://doi.org/10.1056/NEJMoa2202318.

4. E. Armingol*,  H. Baghdassarian*, C. Martino, A. Perez-Lopez, C. Aamodt, R. Knight, N. Lewis. "Context-aware deconvolution of cell-cell communication with Tensor-cell2cell".  Nat. Commun. (June 2022). https://doi.org/10.1038/s41467-022-31369-2.

5. A. Chiang*, H. Baghdassarian*, B. Kellman, B. Bao, J. Sorrentino, C. Liang, C. Kuo, H. Masson, N. Lewis. "Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy". Journal of Biomedical Science. (June 2021). https://doi.org/10.1186/s12929-021-00746-2.

6. B. Kellman*, H. Baghdassarian*, T. Pramparo, I. Shamie, V. Gazestani, A. Begzati, S. Li, S. Nalabolu, S. Murray, L. Lopez, K. Pierce, E. Courchesne, N. Lewis. “Multiple Freeze-Thaw Cycles Lead to a Loss of Consistency in poly(A)-Enriched RNA Sequencing.” BMC Genomics. (Jan. 2021). https://doi.org/10.1186/s12864-021-07381-z.

Co-Author

7.  C. Perez, A. Garmilla, A. Nillson, H. Baghdassarian, K. Gordon, L. Lima, B. Smith, M. Maus, D. Lauffenburger, M. Birnbaum. Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence. In Submission, Cell. https://doi.org/10.1101/2024.04.29.591541.

8. E. Armingol. H. Baghdassarian, N. Lewis. “Next-generation tools for studying cell–cell interactions and communication.” Nat. Rev. Genet. (Jan. 2024). https://doi.org/10.1038/s41576-023-00685-8.

9. E. Armingol, R. Larsen, M. Cequeira, H. Baghdassarian, N. Lewis. “Unraveling the coordinated dynamics of protein- and metabolite-mediated cell-cell communication.” In Preparation. https://doi.org/10.1101/2022.11.02.514917.

10. E. Armingol, A. Ghaddar, C. Joshi, H. Baghdassarian, I. Shamie, J. Chan, H. Her, S. Berhanu, A. Dar, F. Rodriguez-Armstrong, O. Yang, E. O’Rourke, N. Lewis. “Inferring a spatial code of cell-cell interactions and communication across a whole animal body.” PLOS Comp. Bio. (Nov. 2022) . https://doi.org/10.1371/journal.pcbi.1010715.

11. C. Kuo,  A. Chiang, H. Baghdassarian, N. Lewis. "Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation". Cell Systems. (June 2021). https://doi.org/10.1016/j.cels.2021.06.001.

12. H. Pinkard, H. Baghdassarian, A. Mujal, E. Roberts, K. Hu, D. Friedman, I. Malenica, T. Shagan, A. Fries, K. Corbin, M. Krummel*, L. Waller*. “Learned adaptive multiphoton illumination microscopy for large-scale immune response imaging”.  Nat Commun. (March 2021). https://doi.org/10.1038/s41467-021-22246-5.

13. S. Palluk*, D. Arlow*, T. Rond, R. Bector, J. Kang, H. Baghdassarian, A. Truong, P. Kim, A.Singh, N.Hillson, J.Keasling. "De novo DNA synthesis using polymerase-tethered nucleotides." Nat Biotechnol. (June 2018). https://doi.org/10.1038/nbt.4173.

*contributed equally to work

News and Media

1. STAT4 Variants

2. Behind the Paper: Tensor-cell2cell: Unraveling the complex cell-cell communication patterns driving phenotype

Contact and Links:

• Email: hmbaghda at mit dot edu