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Topic

Advancing Cell-Type Annotation and Deconvolution in Human Bronchoalveolar Lavage Through Single-Cell Transcriptomics and Benchmarking Protocols

Department of Mathematics and Statistics

Date & location

• Wednesday, November 12, 2025
• 8:00 A.M.
• Virtual Defence

Examining Committee

Supervisory Committee

• Dr. Xuekui Zhang, Department of Mathematics and Statistics, ӣ��Ӱ�� (Supervisor)
• Dr. Xiaojian Shao, Department of Mathematics and Statistics, UVic (Co-Supervisor)
• Dr. Belaid Moa, Department of Electrical and Computer Engineering, UVic (Outside Member)

External Examiner

• Dr. Pingzhao Hu, Department of Biochemistry, Western University

Chair of Oral Examination

• Dr. Shengyao Lu, Department of Computer Science, UVic

Abstract

Bronchoalveolar lavage (BAL) provides a unique view to analyze immunological aspects of the human lung. single-cell RNA sequencing data (scRNA-seq) of BAL offers a great potential for immunotherapy of lung diseases. Despite promising, challenges persist in identifying disease-relevant high-resolution subcell types, standardizing annotations across studies, and accurately interpreting bulk RNA-seq data. This dissertation is approached from three perspectives. Chapter one serves as the introduction, while chapter two provides the background.

Chapter three presents scRNA-seq data utilized to characterize macrophage and monocyte populations in chronic obstructive pulmonary disease (COPD). The analysis identified dysfunctional alveolar macrophages and hyperinflammatory monocytes, indicating potential therapeutic targets and emphasizing the modulatory effects of inhaled corticosteroids.

The fourth chapter presents a standardized atlas of human BAL cells through the synthesis multiple scRNA-seq datasets, with ensemble auto-annotation tools and reliable cross-study markers. This atlas deals with discrepancies in previous studies and provides a foundation for BAL research.

Chapter five introduces the first true-paired benchmarking study of cellular deconvolution in BAL. Including 30 human BAL samples, each divided into bulk RNA-seq and matched single-cell libraries. After systematically evaluating 15 popular algorithms across multiple references and cell-type resolutions, this study demonstrates a modestly designed pairing strategy substantially improves both benchmark realism and practical accuracy. Our true-paired data, comparative analyses, and three-step protocol provide a blueprint for future deconvolution studies.

Together, these chapters deliver disease insights, community resources, and methodological frameworks that advance the study of lung immunity through both single-cell and bulk transcriptomics.