Photo of Sahand Hormoz,  PhD

Sahand Hormoz, PhD

Dana-Farber Cancer Institute

Dana-Farber Cancer Institute
Phone: (617) 582-7361


sahand_hormoz@hms.harvard.edu

Photo of Sahand Hormoz,  PhD

Dana-Farber Cancer Institute
Phone: (617) 582-7361


sahand_hormoz@hms.harvard.edu

Visualize Collaborations

Sahand Hormoz, PhD

Dana-Farber Cancer Institute

EDUCATIONAL TITLES

  • Associate Professor, Systems Biology, Harvard Medical School
  • Assistant Professor, Data Sciences, Dana-Farber Cancer Institute

HCC PROGRAM AFFILIATION

Research Abstract

During development, for tissue maintenance, and in diseases, as cells proliferate, they transition between functionally and molecularly distinct states. Dysregulation of transitions between cell states can lead to pathologies such as cancer. In many biological systems, we would like to know which transitions can occur, in what sequence, and at what rates. Current single-cell methods have expanded our ability to identify distinct cell states but not our ability to directly measure their dynamics. For example, state of the art single-cell techniques can measure the expression levels of thousands of genes, but they destroy the cells in the process, and provide only static snapshots. In our lab, we combine experiments and theory to understand the complex dynamics of cell state transitions in development and in disease. To do so, we expand and combine emerging experimental techniques. For example, we use time-lapse microscopy to track the lineage history of individual cells as they divide, followed by single-molecule imaging to readout the expression levels of multiple genes in the same cells. We also use synthetic biology to engineer novel genetic circuits that can record histories of cell lineages and major transitions in each cell's own genome. In particular, we use these approaches to uncover the dynamics of differentiation and proliferation of cancer cells in individual patients with a certain types of blood cancer called myeloproliferative neoplasm. In this cancer, intriguingly, the same genetic alteration can result in different disease phenotypes in different patients. We are trying to resolve this disconnect between genotype and phenotype.

Publications from Harvard Catalyst Profiles

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  • Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. Bioinformatics 2023. PubMed
  • Muyas F, Sauer CM, Valle-Inclán JE, Li R, Rahbari R, Mitchell TJ, Hormoz S, Cortés-Ciriano I. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat Biotechnol 2023. PubMed
  • Sevier SA, Hormoz S. Collective polymerase dynamics emerge from DNA supercoiling during transcription. Biophys J 2022; 121:4153-4165. PubMed
  • Van Egeren D, Kamaz B, Liu S, Nguyen M, Reilly CR, Kalyva M, DeAngelo DJ, Galinsky I, Wadleigh M, Winer ES, Luskin MR, Stone RM, Garcia JS, Hobbs GS, Michor F, Cortes-Ciriano I, Mullally A, Hormoz S. Transcriptional differences between JAK2-V617F and wild-type bone marrow cells in patients with myeloproliferative neoplasms. Exp Hematol 2022; 107:14-19. PubMed
  • Budjan C, Liu S, Ranga A, Gayen S, Pourquié O, Hormoz S. Paraxial mesoderm organoids model development of human somites. Elife 2022. PubMed
  • Wang A, Zhang Q, Han Y, Megason S, Hormoz S, Mosaliganti KR, Lam JCK, Li VOK. A novel deep learning-based 3D cell segmentation framework for future image-based disease detection. Sci Rep 2022; 12:342. PubMed
  • Liu S, Nguyen M, Hormoz S. Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling. STAR Protoc 2021; 2:100673. PubMed
  • Sritharan D, Wang S, Hormoz S. Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry. Proc Natl Acad Sci U S A 2021. PubMed
  • Van Egeren D, Escabi J, Nguyen M, Liu S, Reilly CR, Patel S, Kamaz B, Kalyva M, DeAngelo DJ, Galinsky I, Wadleigh M, Winer ES, Luskin MR, Stone RM, Garcia JS, Hobbs GS, Camargo FD, Michor F, Mullally A, Cortes-Ciriano I, Hormoz S. Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms. Cell Stem Cell 2021. PubMed
  • Bowling S, Sritharan D, Osorio FG, Nguyen M, Cheung P, Rodriguez-Fraticelli A, Patel S, Yuan WC, Fujiwara Y, Li BE, Orkin SH, Hormoz S, Camargo FD. An Engineered CRISPR-Cas9 Mouse Line for Simultaneous Readout of Lineage Histories and Gene Expression Profiles in Single Cells. Cell 2020; 181:1410-1422.e27. PubMed
  • Frieda KL, Linton JM, Hormoz S, Choi J, Chow KK, Singer ZS, Budde MW, Elowitz MB, Cai L. Synthetic recording and in situ readout of lineage information in single cells. Nature 2017; 541:107-111. PubMed
  • Hormoz S, Singer ZS, Linton JM, Antebi YE, Shraiman BI, Elowitz MB. Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Syst 2016; 3:419-433.e8. PubMed
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