Liu, JiaWang, XiaoHe, Yichun2025-09-1820252025-06-052025He, Yichun. 2025. Advancing Molecular and Functional Understanding of Cells with Artificial Intelligence. Doctoral Dissertation, Harvard University Graduate School of Arts and Sciences.32038985https://dash.harvard.edu/handle/1/42719738Rapid advancements in biotechnology are transforming our ability to measure biological systems across multiple modalities and scales. On the molecular level, spatially resolved single-cell transcriptomics enables comprehensive profiling of gene expression while preserving the spatial architecture of tissues. On the functional level, flexible brain-machine interfaces permit stable, long-term recordings of single-neuron activity throughout behavioral learning and disease progression. These innovations have increasingly shifted the life sciences toward a data-centric paradigm. However, traditional computational modeling approaches remain limited in their capacity to extract meaningful biological insights from heterogeneous, high-dimensional data, especially for decoding complex cell states and functions across space and time. To address this critical gap, this dissertation introduces a suite of computational methods that integrate artificial intelligence and machine learning (AI/ML) with cutting-edge biotechnologies. These methods are designed to interpret large-scale, multimodal biological data and feed insights back into experimental design for iterative discovery. Chapter 2 presents ClusterMap, a spatially informed, unsupervised clustering framework for single-cell and tissue segmentation directly from in situ transcriptomic data. Building on this, Chapter 3 builds a comprehensive spatial atlas of the mouse central nervous system by integrating single-cell gene expression and spatial data at subcellular resolution. To generalize spatial analysis across datasets and technologies, Chapter 4 introduces FuseMap, a universal deep-learning framework that harmonizes multiple brain atlases into a common coordinate framework, enabling gene imputation, tissue region annotation, and cross-dataset integration. Expanding beyond transcriptomics, Chapter 5 introduces AutoSort, a real-time multimodal spike sorting algorithm for stable long-term neural recordings, and UnitedNet, a multi-task learning model that jointly performs cell-type identification, cross-modal prediction, and feature relevance discovery across diverse single-cell modalities. In summary, this dissertation presents novel AI-powered frameworks that bridge molecular and functional modalities at the single-cell level. These approaches not only enhance our ability to decode the complex architecture and dynamics of biological systems but also provide a foundation for future integrative studies in development, disease, and therapeutic response.application/pdfenBioengineeringThesis or Dissertation2025-09-180000-0001-9573-2682