From Observation to Perturbation: Dissecting Cell State Transitions in Immune and Cancer Cells
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tong-evelyntg-phd-hst-2026-thesis.pdf
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Author(s)
Tong, Yuzhou Evelyn
Advisor(s)
Shalek, Alex K.
Date Issued
February 2026
Publisher
Massachusetts Institute of Technology
Abstract
Cell state transitions are central to both immune function and disease progression, yet the mechanisms that govern these transitions and their therapeutic implications remain poorly understood. This thesis bridges observational and perturbational approaches to dissect how immune and cancer cells acquire, maintain and functionally leverage dynamic transcriptional states.
In the first part, we use scRNA-Seq to characterize immune cell states across different contexts. In hematologic malignancy, we identify that myeloid dysfunction driven by inhibitory signals can occur in pre-malignancy and is reversible. Using an in vivo colon cancer model, we demonstrate that activating myeloid cells with a TLR7/8 agonist can restore antitumor immunity. Extending beyond cancer, we investigate how influenza vaccination and pneumococcal colonization jointly shape lung immunity, finding that these pathogen exposures can destabilize the macrophage homeostasis in the lower respiratory tract. Together, these studies highlight that immune cell state transitions are dynamic, context-dependent and functionally relevant.
Recognizing that observational studies alone cannot determine upstream regulators and downstream consequences of these states, the second part of the thesis introduces a high-throughput experimental and computational framework for mapping cell states to functions. By screening cancer cell lines with a pooled human transcription factor overexpression library under inflammatory and therapeutic pressures, we identify multiple transcription factors that promote cell state transitions associated with selective fitness advantages. By linking cell states to cellular fitness and clinical features, this platform enables systematic mapping of transcription factors to cell state transitions and functional outputs, moving beyond observational transcriptomics toward function-guided, actionable insights.
MIT Department
Harvard-MIT Program in Health Sciences and Technology
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