Marmar Moussa
University of Oklahoma, OK·Computer Science·Early-career
Cancer is shaped by two forces that single-cell measurements can now track in detail: tumor cells that keep evolving, and immune cells that try to recognize and destroy them. Making sense of these huge, noisy datasets requires new statistics and algorithms.
- Fields
Computer Science
Genetics
Microbiology & Immunology
- Topics
single-cell sequencing
spatial transcriptomics
spatially resolved omics
- Grants
- 3 known grants, through Jan 2031
What they work on
- Model and predict which T-cell receptors are activated by neoepitope vaccination in MethA and MC38 mouse tumor models, using single-cell TCR sequencing paired with DNA-barcoded peptide–MHC dextramers that report what each cell binds.
- Test how the choice of adjuvant, a vaccine additive that boosts immunity, affects the immune response to neoepitope-based peptide cancer vaccines in mice.
- Develop a two-dimensional statistical testing algorithm that finds spatial gene expression patterns in single-cell tissue data, such as patterns that mark tumor progression or tissue remodeling.
- Build a publicly available interactive tool for analyzing spatially resolved single-cell datasets and for describing how cells change and interact across tissue space.
- Develop algorithms that jointly reconstruct clone frequencies and phased copy number and single-nucleotide variant profiles from low-coverage single-cell DNA sequencing of tumors.
- Infer tumor phylogenies and fitness and mutability landscapes with a bipartition-based median-tree approach and convex optimization, then validate on simulated, public and UConn Health datasets.
More about the research
The lab's projects build computational methods for single-cell data in three settings: predicting which T cells respond to cancer vaccines, finding gene activity patterns in their spatial position within tissue, and reconstructing how tumors evolve.
Why it matters
These methods could help cancer researchers design more effective neoepitope-based cancer vaccines by identifying the T-cell receptor features linked to strong antitumor responses. Spatial analysis tools could reveal gene patterns that mark tumor progression in tissue samples. Phylodynamic software could let scientists measure how specific genome alterations affect how tumors grow and evolve, which informs the study of cancer progression and therapy escape.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
TCR-antigen specificities and phenotypes in peptide-based cancer vaccines
NIH · May 2026 – Jan 2031
CAREER: Computational and statistical methods for studying single cell genomics data in the context of tissue organization
NSF · May 2025 – Apr 2030
Collaborative Research: III: Medium: Algorithms for scalable inference and phylodynamic analysis of tumor haplotypes using low-coverage single cell sequencing data
NSF · Jul 2023 – Sep 2026
Closest labs
Peter Aidan Savage
University of Chicago, IL·Microbiology & Immunology
Define the biophysical and structural basis by which T cell receptors from regulatory and conventional CD4+ T cells recognize the same self-peptide displayed on MHC class II, testing whether a more centered binding mode increases signaling sensitivity.
Chris Kieslich
Georgia Institute of Technology, GA·Computer Science
Build predictors of antigen processing that estimate which peptide fragments a cell actually produces when it degrades a protein, combining molecular models of the cutting machinery with statistical analysis of large public peptide datasets.
Hussein Sultan
Washington University in St Louis, MO·Microbiology & Immunology
Determine how the EBI3 subunit of the suppressive cytokine IL-35 drives induction of Tr1 regulatory CD4 T cells, using IL-35 reporter mice in established tumor models to identify which cells produce it.
The school
University of Oklahoma: 11 labs on PI Pages, $474M a year on research, 225 research doctorates a year.
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