Advances in multiplex fluorescence imaging now allow the measurement of more than 50protein markers in whole tissue sections at single-cell resolution. This promises to reveal tumor biology at an unprecedented level of detail, both in undisturbed growth and in therapy. However, to quantitatively analyze these images, the images must be broken down into the basic units of tumor biology: single cells and their types. In this study, we applied a graph-based unsupervised clustering method, Leiden, to perform cell type identification in highly multiplexed fluorescence images, and based on the annotated images, we ran the tumor microenvironment niches analysis in order to resolve the recurring patterns of tumor microarchitecture. This thesis first introduces several potentially feasible clustering methods selected based on the structure of the datasets studied. The performance and stability of these clustering methods were compared. The project involved benchmarking different dimensionality reduction and clustering techniques on manually annotated reference datasets and healthy tissue with known cellular composition. It was ultimately determined that appropriate data transformations combined with Leiden clustering methods with proper parameters could automatically identify cells in a way coherent with established marker profiles. The results imply that Leiden clustering can also identify clusters of cells with novel marker combinations. Careful examination of the multiplex images shows that the markers are indeed found in the tumor, leading to new hypotheses regarding tumor biology. Tumor microenvironment niches analysis found several archetypal niches with specific cellular composition, indicating active accumulation of immune cells after radiotherapy, and the less vascularized feature of rebound glioblastomas after treatment. We hope to further validate our analysis to provide new insights into the pathological process of glioblastoma. In future research, the analysis pipeline is planned to be improved so that it can be robustly used to analyze the growing data of multiplexed tumor images, both in mouse cancer models or patient samples.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-478512 |
| Date | January 2022 |
| Creators | Kang, Ziqi |
| Publisher | Uppsala universitet, Institutionen för biologisk grundutbildning, Department of Cellular and Molecular Biology, Karolinska Institutet & Science for Life Laboratory |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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