The functional plasticity of the immune system is central to human health and disease, yet the high-resolution assessment of immune cell states remains a significant challenge. The advent of single-cell sequencing has revolutionized this research field by enabling the profiling of individual cellular transcriptomes. However, the resulting high-dimensional datasets are frequently characterized by extreme sparsity, stochastic noise, and technical batch effects that can obscure underlying biological signals. Navigating this complexity necessitates the development of sophisticated computational frameworks capable of modeling of single-cell data. This dissertation presents comprehensive bioinformatics frameworks for the characterization, representation, and prediction of immune cell states by integrating single-cell sequencing and advanced machine learning architectures.
The first pillar, characterization, is demonstrated by Scupa, which leverages single-cell foundation models for a unified assessment of cytokine-driven polarization. Scupa demonstrates that immune polarization exists on a conserved functional spectrum across diverse physiological and pathological contexts. The second pillar, representation, is addressed through FADVI, a variational autoencoder framework utilizing factorized disentanglement to isolate biological signals from technical batch effects. This provides a robust foundation for data integration. The translational utility of these methods is demonstrated in a study of regional immunotherapy delivery. By characterizing the immune landscape in response to vaccination and αCTLA4 delivery to non-tumor-draining lymph nodes, we identified specific T cell subpopulations as primary mediators of anti-tumor efficacy, highlighting the necessity of state-specific resolution. The third pillar, prediction, is exemplified by Turep, a deep-learning framework for cross-cancer tumor-reactive T cell identification. Turep identifies tumor-reactive clones in an antigen-agnostic manner and reveals active tumor-recognition regions within the tissue architecture when extended to spatial transcriptomics.
Collectively, these frameworks mark a pivotal transition from descriptive analysis toward inferential systems biology. They establish benchmarks for disentangled representation and functional scoring using generative modeling and foundation models. For the broader biology community, these methodologies provide high-fidelity tools for navigating immune plasticity, enabling researchers to move beyond static cell-type identities to identify the specific, fluid functional states that drive disease progression and therapeutic response.
","abstract_html":"<p>The functional plasticity of the immune system is central to human health and disease, yet the high-resolution assessment of immune cell states remains a significant challenge. The advent of single-cell sequencing has revolutionized this research field by enabling the profiling of individual cellular transcriptomes. However, the resulting high-dimensional datasets are frequently characterized by extreme sparsity, stochastic noise, and technical batch effects that can obscure underlying biological signals. Navigating this complexity necessitates the development of sophisticated computational frameworks capable of modeling of single-cell data. This dissertation presents comprehensive bioinformatics frameworks for the characterization, representation, and prediction of immune cell states by integrating single-cell sequencing and advanced machine learning architectures.</p> <p>The first pillar, characterization, is demonstrated by Scupa, which leverages single-cell foundation models for a unified assessment of cytokine-driven polarization. Scupa demonstrates that immune polarization exists on a conserved functional spectrum across diverse physiological and pathological contexts. The second pillar, representation, is addressed through FADVI, a variational autoencoder framework utilizing factorized disentanglement to isolate biological signals from technical batch effects. This provides a robust foundation for data integration. The translational utility of these methods is demonstrated in a study of regional immunotherapy delivery. By characterizing the immune landscape in response to vaccination and αCTLA4 delivery to non-tumor-draining lymph nodes, we identified specific T cell subpopulations as primary mediators of anti-tumor efficacy, highlighting the necessity of state-specific resolution. The third pillar, prediction, is exemplified by Turep, a deep-learning framework for cross-cancer tumor-reactive T cell identification. Turep identifies tumor-reactive clones in an antigen-agnostic manner and reveals active tumor-recognition regions within the tissue architecture when extended to spatial transcriptomics.</p> <p>Collectively, these frameworks mark a pivotal transition from descriptive analysis toward inferential systems biology. They establish benchmarks for disentangled representation and functional scoring using generative modeling and foundation models. For the broader biology community, these methodologies provide high-fidelity tools for navigating immune plasticity, enabling researchers to move beyond static cell-type identities to identify the specific, fluid functional states that drive disease progression and therapeutic response.</p>","abstract_has_math":false,"creators":["Liu, Wendao","0000-0002-5124-9338
"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation (PhD)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Zhongming Zhao","Peng Wei","Eva Sevick-Muraca"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T07:00:00Z","date_published":"2026-05-01T07:00:00Z","updated_at":"2026-07-24T05:50:47Z","subjects":["Machine learning","single-cell sequencing","immune cell state","cancer immunology","Bioinformatics","Cancer Biology","Cell Biology","Computational Biology","Immunity","Systems Biology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1512","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhongming Zhao","Peng Wei","Eva Sevick-Muraca"]},{"key":"dc:creator","label":"Author","values":["Liu, Wendao","0000-0002-5124-9338
"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2027-04-17T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation (PhD)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning","single-cell sequencing","immune cell state","cancer immunology","Bioinformatics","Cancer Biology","Cell Biology","Computational Biology","Immunity","Systems Biology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1512"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The functional plasticity of the immune system is central to human health and disease, yet the high-resolution assessment of immune cell states remains a significant challenge. The advent of single-cell sequencing has revolutionized this research field by enabling the profiling of individual cellular transcriptomes. However, the resulting high-dimensional datasets are frequently characterized by extreme sparsity, stochastic noise, and technical batch effects that can obscure underlying biological signals. Navigating this complexity necessitates the development of sophisticated computational frameworks capable of modeling of single-cell data. This dissertation presents comprehensive bioinformatics frameworks for the characterization, representation, and prediction of immune cell states by integrating single-cell sequencing and advanced machine learning architectures.
The first pillar, characterization, is demonstrated by Scupa, which leverages single-cell foundation models for a unified assessment of cytokine-driven polarization. Scupa demonstrates that immune polarization exists on a conserved functional spectrum across diverse physiological and pathological contexts. The second pillar, representation, is addressed through FADVI, a variational autoencoder framework utilizing factorized disentanglement to isolate biological signals from technical batch effects. This provides a robust foundation for data integration. The translational utility of these methods is demonstrated in a study of regional immunotherapy delivery. By characterizing the immune landscape in response to vaccination and αCTLA4 delivery to non-tumor-draining lymph nodes, we identified specific T cell subpopulations as primary mediators of anti-tumor efficacy, highlighting the necessity of state-specific resolution. The third pillar, prediction, is exemplified by Turep, a deep-learning framework for cross-cancer tumor-reactive T cell identification. Turep identifies tumor-reactive clones in an antigen-agnostic manner and reveals active tumor-recognition regions within the tissue architecture when extended to spatial transcriptomics.
Collectively, these frameworks mark a pivotal transition from descriptive analysis toward inferential systems biology. They establish benchmarks for disentangled representation and functional scoring using generative modeling and foundation models. For the broader biology community, these methodologies provide high-fidelity tools for navigating immune plasticity, enabling researchers to move beyond static cell-type identities to identify the specific, fluid functional states that drive disease progression and therapeutic response.
"]},{"key":"dc:title","label":"Title","values":["Characterization, representation, and prediction of immune cell states using single-cell sequencing and machine learning"]}]}],"canonical_facts":{"dc:contributor":["Zhongming Zhao","Peng Wei","Eva Sevick-Muraca"],"dc:creator":["Liu, Wendao","0000-0002-5124-9338
"],"dc:date.available":["2027-04-17T07:00:00Z"],"dc:description.abstract":["The functional plasticity of the immune system is central to human health and disease, yet the high-resolution assessment of immune cell states remains a significant challenge. The advent of single-cell sequencing has revolutionized this research field by enabling the profiling of individual cellular transcriptomes. However, the resulting high-dimensional datasets are frequently characterized by extreme sparsity, stochastic noise, and technical batch effects that can obscure underlying biological signals. Navigating this complexity necessitates the development of sophisticated computational frameworks capable of modeling of single-cell data. This dissertation presents comprehensive bioinformatics frameworks for the characterization, representation, and prediction of immune cell states by integrating single-cell sequencing and advanced machine learning architectures.
The first pillar, characterization, is demonstrated by Scupa, which leverages single-cell foundation models for a unified assessment of cytokine-driven polarization. Scupa demonstrates that immune polarization exists on a conserved functional spectrum across diverse physiological and pathological contexts. The second pillar, representation, is addressed through FADVI, a variational autoencoder framework utilizing factorized disentanglement to isolate biological signals from technical batch effects. This provides a robust foundation for data integration. The translational utility of these methods is demonstrated in a study of regional immunotherapy delivery. By characterizing the immune landscape in response to vaccination and αCTLA4 delivery to non-tumor-draining lymph nodes, we identified specific T cell subpopulations as primary mediators of anti-tumor efficacy, highlighting the necessity of state-specific resolution. The third pillar, prediction, is exemplified by Turep, a deep-learning framework for cross-cancer tumor-reactive T cell identification. Turep identifies tumor-reactive clones in an antigen-agnostic manner and reveals active tumor-recognition regions within the tissue architecture when extended to spatial transcriptomics.
Collectively, these frameworks mark a pivotal transition from descriptive analysis toward inferential systems biology. They establish benchmarks for disentangled representation and functional scoring using generative modeling and foundation models. For the broader biology community, these methodologies provide high-fidelity tools for navigating immune plasticity, enabling researchers to move beyond static cell-type identities to identify the specific, fluid functional states that drive disease progression and therapeutic response.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1512"],"dc:subject":["Machine learning","single-cell sequencing","immune cell state","cancer immunology","Bioinformatics","Cancer Biology","Cell Biology","Computational Biology","Immunity","Systems Biology"],"dc:title":["Characterization, representation, and prediction of immune cell states using single-cell sequencing and machine learning"],"thesis:degree_level":["Dissertation (PhD)"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T05:50:47Z"}