Browsing by Author "Liang, Zhihan"

High-grade serous ovarian cancer (HGSC) is the most aggressive subtype of ovarian cancer. Most patients are diagnosed at an advanced stage, resulting in a poor prognosis. Although some targeted therapies have made good progress, immune checkpoint inhibitors (ICIs) still show limited efficacy in HGSC treatment. This is closely associated with the high heterogeneity and unique immunosuppressive tumor microenvironment (TME) of HGSC. As local immune centers in the TME, tertiary lymphoid structures (TLSs) contain diverse types of immune cells and play a crucial role in the anti-tumor immune process. Nonetheless, the spatial immune landscape of TLSs and key gene expression signatures affecting prognosis have not been fully elucidated. In recent years, spatially resolved gene expression profiling of clinical samples has provided insights into the transcriptome at high resolution. However, despite revealing a series of response mechanisms of anti-tumor immunity, these methods still have many limitations in non-single cell resolution, specific tissue sample types, and high cost. Here in this thesis, I established Nano-Pick, a novel method based on precise microtissue extraction and the nCounter analysis system. Nano-Pick enables cost-effective and rapid spatial gene expression profiling using formalin-fixed paraffin-embedded (FFPE) samples. All TLSs-enriched FFPE slides were obtained from patient-derived HGSC samples at Helsinki University Hospital (HUH). With the help of tissue cyclic immunofluorescence (t-CycIF), a highly multiplexed imaging method, selected regions of interests (ROIs) were annotated in different zones of TLSs. These ROIs were accurately transferred to adjacent tissue sections, guiding the target region selection for microtissue extraction. An improved immunofluorescence staining method was used to successfully label cancer cells, macrophages, and immune cells without antigen retrieval. With the optimized combination of parameters, efficient and consistent precise picking for different cell types was achieved. mRNA extracted from the collected microtissues was validated by qPCR for the range of pre-amplification cycles, confirming its suitability for nCounter gene panel analysis. Therefore, Nano-Pick offers the opportunity for higher-resolution spatial analysis and interactions of immune cell populations in the TME, especially in the TLSs. This low-cost, reliable and sensitive spatial gene expression profiling method has broad application prospects in many fields, with the potential to advance the development of precision medicine.